Methods and systems for controlling access to computing resources based on known security vulnerabilities

ABSTRACT

Methods and systems are provided for fine tuning access control by remote, endpoint systems to host systems. Multiple conditions/states of one or both of the endpoint and host systems are monitored, collected and fed to an analysis engine. Using one or more of many different flexible, adaptable models and algorithms, an analysis engine analyzes the status of the conditions and makes decisions in accordance with pre-established policies and rules regarding the security of the endpoint and host system. Based upon the conditions, the policies, and the analytical results, actions are initiated regarding security and access matters. In one described embodiment of the invention, the monitored conditions include software vulnerabilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/752,424 filed Dec. 21, 2005, incorporated herein in its entirety.

This application is related to co-pending U.S. Patent Application No.[attorney docket number: 1291U004US00] Titled: Methods And Systems ForIntelligently Controlling Access to Computing Resources, filed on samedate herewith, the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates generally to electronic computer security,and more specifically to methods and systems for controlling access tocomputing resources based on known computing security vulnerabilities.

BACKGROUND OF THE INVENTION

Electronic communication is becoming the industry standard for businesscommunications. Increasingly, office files, design documents, employeework products, company information, and most other important businessinformation is being created and stored electronically on desktopcomputers, laptop computers, handheld computing devices (collectively‘personal computing device’ or ‘computing device’) and company networks.At work, employees access such networks, along with their associatedcorporate computing resources from their local computing device, on adaily basis in order to perform their jobs. Away from work, employeessimilarly access such networks and resources, typically through remoteconnections. Numerous types of electronic connections are ubiquitous inthe industry and well known to the reader, for example: dial-upconnections, wireless connections, high-speed connections of varioustypes, virtual private network connections, and others.

Security of such electronic networks has become a recognized,challenging and growing problem. Inappropriate and/or unauthorizedaccess to such electronic networks, and the computing resourcesaccessible there through, raises the risk of theft, destruction and/orunauthorized modification of valuable data, information and intellectualproperty. While local, on-site, security can be easily controlledthrough physical constraints, remote electronic access to such networksand computing resources, typically referred to as endpoint accesscontrol, is a more challenging problem.

Endpoint access controls have followed an incremental, evolutionarypath. Prior to the storage of sensitive data and the recognition of thesecurity issues associated therewith, there were no endpoint accesscontrols. However, security issues such as data theft, unauthorizedaccess, fraud, etc., and the resulting concerns, created anindustry-wide demand for security solutions.

The first generation of endpoint access control included operatingsystem services that controlled user access to one or more systemresources, such as applications, data files, configuration settings,etc. Users were permitted or denied access to these resources based on avariety of factors, such as their login ID (which was authenticatedusing a secret) and a secured profile of policy settings identifyingpermissions and/or restrictions. These permissions were generally staticin that they were not context sensitive in any other dimension than theuser ID. There was no consideration of environmental factors. Thisstatic nature of security services embedded into the operating systemremains relatively unchanged in many environments, to the present day.

In the next step in the evolutionary path of endpoint security control,a series of point solutions were created that address point securityconcerns by providing point access control capabilities. Examples ofthese point solutions include: personal firewalls that restrict inboundand/or outbound access to specified applications, ports, addressesand/or communication protocols; antivirus agents, anti-spyware agentsand application white-list management agents that monitor, detect and/orrestrict access to specific system resources such as memory, registrykeys, etc.; software update agents that automatically update anapplication if it is not a specified version; data encryption agentsthat encrypt specific files, the complete contents of specific folders,etc.; and physical access control agents that restrict access to floppydrives, USB drives, CD-ROM drives, etc. These security agents areone-dimensional in that they look at a single aspect of the endpoint'ssecurity posture and make decisions on that basis. There is nointegration of data across these security agents—all of these securitysolutions operate autonomously and completely independent of each other,with little or no communications between them or awareness of the stateof other applications running on the endpoint. As with operating systemsecurity services, these point solutions are also static. The businesslogic and configurations of these point solutions are not contextsensitive. They typically apply the same rules regardless of the userID, user location, time of day, presence or absence of other securityapplications on the endpoint, configuration and state of other securityor management applications on the endpoint, etc. While providingrelatively stable and secure access control, such static endpointcontrols remain inflexible and not adaptable to user and business needs.They are very much in use today in many environments.

In the most recent evolutionary step, context awareness has beenintroduced into the field of endpoint security control. Functionalexamples of context awareness capabilities on the market today include:if a named application is not running or is not of a specified minimumversion, access to network connectivity or certain applications will berestricted or blocked altogether; if a user is in location X (asdetermined by an assigned IP address, reachability of a network host, orsome other method of automated location determination), the user ispermitted outbound access using application X and Y to network serverson subnet Z, however if the user is in location Y (alternatively anunknown location), the user is permitted outbound access usingapplication X and W to network servers on subnet V. In each of theseexamples, access to a resource (in the first case an application, in thesecond case the network and communications protocols) is contextsensitive in the sense that the access privilege is conditional on thecurrent state of the endpoint (in the first case a certain applicationrunning, in the second case the current location). However thesesolutions are limited in that they are only able to assess a limited setof inputs and affect a narrow set of access privileges. Additionally,once an access privilege has been granted, the decision is rarelyrevisited over the life of the user's connection or access session, i.e.they could come out of compliance subsequent to granting of access andwill still retain access.

Today's access control solutions still lack significant functions andcapabilities. As one example, they lack the ability to formcontext-based access control decisions using as decision inputs stateinformation provided by point solutions that are not context aware.Further lacking is the ability to collect endpoint state informationfrom multiple point solutions, collect endpoint state information fromthe environment itself (e.g. information obtained from the operatingsystem), and integrate the collected information to form a higher-levelholistic and intelligent view of the overall endpoint state.

Today's solutions further fail to provide extensibility of the endpointstate information integration function so as to enable the collectionand integration of endpoint state information from a wide range ofexisting and future point solutions, applications and the endpointenvironment itself. They lack the ability to define and enforce moregranular access control permissions and restrictions, including theextensibility of this granular access control function to future accesscontrol objectives.

Today's endpoint securities solutions do not provide the ability todefine conditional, parameter-based business logic with flexiblecompliance models. They lack the ability to define via configurationsettings parameter values for different users and user groups, andfurther lack the ability to optionally and selectively notify an enduser when access control restrictions are being enforced on theirendpoint.

Further desirable, and lacking, are useful, functional, managementreports as well as dynamic, functional and user-friendly access controlcapabilities.

It will thus be seen that today's endpoint security control systems lackmany functionalities and capabilities of importance both to hands-onusers and their employers.

SUMMARY OF THE INVENTION

There are provided herein methods and systems for flexibly managingcorporate security policies, typically to control access to local orremote computing resources.

In one embodiment of the invention there are provided methods andsystems for controlling the operation of a computing system in responseto a security vulnerability, one exemplary method comprising: thecomputing system running software subject to at least one securityvulnerability; establishing a policy based on the status of the at leastone security vulnerability including at least one rule and an analysismethod for determining compliance with the rule; receiving informationrelating to the status of the at least one security vulnerability of thesoftware program; processing the information relating to the statususing the analysis method; determining, based on the processing, thecompliance of the at least one security vulnerability in relation to therule; and controlling, based on the determining, the operation of thecomputing system.

In another embodiment of the invention there are provided methods andsystems for controlling the access of an endpoint computing system to ahost computing system in response to a security vulnerability, anexemplary method comprising: identifying within at least one of theendpoint and host systems a plurality of conditions, each conditionhaving a state; operating on at least one of the host computing systemand the endpoint computing system a software program subject to at leastone security vulnerability; establishing a policy based on the status ofthe at least one security vulnerability and the state of each of theplurality of conditions, the policy including at least one rule and ananalysis method for determining compliance with the rule; receivinginformation relating to the status of the at least one known securityvulnerability of the software program; receiving information relating tothe state of each of the plurality of conditions; processing theinformation relating to the status of the at least one known securityvulnerability and the state of each of the plurality of conditions usingthe analysis method; determining, based on the processing, thecompliance of the at least one security vulnerability and the pluralityof conditions with the rule; and controlling, based on the determining,access of the endpoint system to a resource of the host computingsystem.

In another embodiment of the invention there are provided methods andsystems for generating signals to control the access of an endpointcomputing system to a resource in a host computing system, an exemplarymethod comprising: collecting a state for each of a plurality ofconditions in at least one of the endpoint computing system and the hostcomputing system; collecting a status of a known security vulnerabilityfor a software program operating on at least one of the host computingsystem and the endpoint computing system; identifying a policy fordetermining access of the endpoint computing system to the resource, thepolicy including at least one rule and an analysis method fordetermining compliance with the rule; processing, using the analysismethod, the state of each of the plurality of conditions and the statusof the known security vulnerability; determining, based upon theprocessing, if the conditions and the known security vulnerability arein compliance with the rule; and generating, based upon the determining,a signal usable to control the access of the endpoint computing systemto the resource.

In yet another embodiment of the invention there are provided methodsand systems for developing a compliance policy to control the access ofan endpoint computing system to a resource in a host computing system,an exemplary method comprising: identifying a plurality of conditions inat least one of the endpoint computing system and the host computingsystem, each of the plurality of conditions including an associatedstate, at least one of the plurality of conditions relating to a risk ofa known security vulnerability; and developing a policy for determiningthe access of the endpoint computing system to the resource, the policyincluding a rule and at least one analysis method for processing thestates of the plurality of conditions to determine if the plurality ofconditions are in compliance with the rule.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

These and other objects, features and advantages of the presentinvention will become apparent from a consideration of the followingDetailed Description Of The Invention in conjunction with the drawingFigures, in which:

FIG. 1 is a block diagram showing features of a security compliancesystem in accordance with one embodiment of the present invention;

FIG. 2 is a flow chart showing a process for managing securitycompliance in accordance with an embodiment of the invention;

FIG. 3, is a functional block diagram showing the interaction of agents,managers, monitors and compliance engine in a security compliancesystem;

FIG. 4 is a flow chart showing the flow of information between agents,managers, monitors, and the policy management system;

FIG. 5 is a block diagram showing an alternate embodiment of theinvention wherein various components of the policy management system areincorporated with in the other computing systems;

FIG. 6 is a flow chart showing a process for integrating known securityrisks into a compliance system; and

FIG. 7 is a flow chart showing the operation of the analysis engine toanalyze agent data and develop a compliance policy.

DETAILED DESCRIPTION OF THE INVENTION

As used here in, examples and illustrations, as well as descriptiveterminology such as “exemplary” and “illustrative” and variants thereof,are descriptive and non limiting.

For purposes of describing the present invention, the followingspecification is arranged topically, in accordance with the followingtopics:

-   Overview-   Description Of The System-   Establishing Agents And Managers-   Establishing Rules And Policies-   Administrator Policy Configuration-   Integration With Vulnerability Scoring Systems-   Analyzing Agent—Collected Condition Data-   EndPoint Compliance Assessment Algorithms    -   Matrix Analysis Algorithm    -   Business Rules-Based Analytical Model For Policy Enforcement    -   Boolean Table-Based Analytic Model For Policy Enforcement    -   Scoring-Based Analytical Model For Policy Enforcement    -   Individual Agent Score Threshold Analysis And Enforcement    -   Composite Agent Scoring, Threshold Analysis And Enforcement    -   Complementary Individual And Composite Agent Scoring, Threshold        Analysis And Enforcement    -   Single Level Versus Multi-Level Agent Scoring, Threshold        Analysis And Enforcement    -   Continuous Reporting Versus Exception Reporting Threshold        Analysis and Enforcement    -   Matrix Algebra-Based Analytical Model for Policy Enforcement    -   Context-Sensitive Threshold and Weighting Adjustments to        Quantitative Analytical Models for Policy Enforcement    -   Statistics-Based Analytical Model for Policy Enforcement    -   Data Summary-Based Statistical Analysis Methods    -   Mean-Based Analysis Method    -   Moving Average-Based Statistical Analysis Method    -   Median-Based Statistical Analysis Method    -   Mode-Based Statistical Analysis Method    -   Geometric Mean-Based Statistical Analysis Method    -   Rate-Based Statistical Analysis Method    -   Acceleration Rate-Based Statistical Analysis Method    -   Variability-Based Statistical Analysis Methods        -   Min-Based, Max-Based and Range-Based Statistical Analysis            Method        -   Standard Deviation-Based Statistical Analysis Method        -   Coefficient of Variation-Based Statistical Analysis Method        -   Number of Occurrences-Based Statistical Analysis Method        -   Occurrence Frequency-Based Statistical Analysis Method        -   Cumulative Distribution-Based Statistical Analysis Method        -   Sampling Distribution-Based Statistical Analysis Method        -   Sampling Distribution-Based Statistical Analysis Method    -   Linear Regression-Based Analysis Method    -   Filtering Analysis    -   Application of Methods to All Endpoint State Data Elements    -   Application of Methods to Non-Numeric Endpoint State Information    -   Application of Analytical Methods to Composite Endpoint        Compliance Assessments    -   Exception Reporting of Analyses Result    -   Non-Exclusivity of Analyses Methods    -   Combining Analyses Methods-   Real Time Adjustment of Sampling Frequency-   Managing Endpoint and Host Operation-   Communication of Endpoint State Information, Endpoint Compliance    Analysis Results And/Or Compliance Actions to a Remote Computer-   Implementation Method 1—Endpoint system Only-   Implementation Method 2—Centralized endpoint system policy    management-   Implementation Method 3—Centralized host system policy management-   Implementation Method 4—Centralized analysis engine and compliance    analysis of individual systems-   Implementation Method 5—Centralized analysis engine and compliance    analysis of multiple systems-   Implementation Method 6—Policy management system as in-band access    control mechanism-   Data Sharing-   Remote Administrator Notification and Control

Overview

The present invention provides new and improved methods and systems forflexibly monitoring, evaluating, and initiating actions to enforcesecurity compliance policies. As will be seen from a consideration ofthe detailed description of the invention, provided below, benefits andadvantages of the present invention include:

-   The collection of a wide range of endpoint state information. The    enumeration of state policies regarding preferred, required and    prohibited states.-   The enumeration of action policies regarding required, permitted and    prohibited actions to take when the endpoint is partially or    entirely in or out of compliance with state policies.-   An analysis engine enabling comparing current states, state policies    and action policies and reaching decisions on actions to permit,    prevent, or automatically initiate.-   A flexible methodology for assigning numerical values to current    state information, state policies and action policies so that a    variety of quantitatively-based analysis models can be used to    determine security compliance.-   An enforcement capability that can operate persistently, constantly    measuring compliance, with an ability to dynamically adjust access    privileges subsequent to an initial granting of privileges.-   An ability to create and adjust a ‘sliding scale’ having different    levels of overall security risk tolerance or conversely an overall    minimum security threshold that allows or prevents access to    specific hardware, software and/or computing resources depending on    the degree of compliance with level-specific security policies and    in particular the specific types of noncompliance that exist at each    level.-   A compliance analysis engine that supports use of a range of    different analytical methods and models so that optimum models can    be invoked and applied, depending on situational factors.-   The initiation of and controlled access to a wide range of software    and hardware actions.

Description of the System

As used here, the terms “illustrative,” “example,” “includes,” andvariants thereof are exemplary and not exclusive or otherwise limiting.

With reference now to FIG. 1, there is shown there is shown a system100, including a host system 102, an endpoint system 104, and a policymanagement system 106. In accordance with the present invention andconventional use, host system 102 comprises a secure, access-controlledprocessing system where-to remote systems such as endpoint system 104connect to access data, processing capacity and host-accessibleresources. Policy management system 106 provides rules and policiesconcerning the connection of remote endpoint systems 104 to host system102. Host system 102, endpoint system 104, and policy management system106, are interconnected to communicate through a conventional electronicnetwork 108, such as the Internet.

Considering in detail host system 102, the system is seen to include, ina conventional manner, a processor and user & communications interface102A, as well as conventional storage components 102B, operating systemsand software (typically contained in storage and operated by theprocessor) and other conventional components. Further associated withhost system 102 are a variety of resources, indicated at 102G,accessible directly or indirectly through the host, including, forexample: user data, user applications, physical ports, data storagedevices, dial adaptors, network interfaces, and other resources as willbe apparent to the reader. Further contained within host 102 are aplurality of conditions 102F. These conditions are monitored by agents102E, the agents collecting and transmitting information to agentmanagers 102D for aggregation by agent monitor 102F. The variousconditions, as well as the agent functions, are described in detailherein below. Host 102 may comprise, for example, a processing system ofthe type typically owned, managed and/or operated by a business tosupport the operation of its employees. It may comprise a server,enterprise system, personal computer, laptop, personal digitalassistant, mobile communications device such as a ‘smart’ telephone, orany other type of remotely accessible system. In a conventional manner,host system 102 may include conventional security features forcontrolling access to the data and resources thereon.

Host system 102 may be consolidated at a single location or comprise aplurality of systems dispersed over multiple locations.

Continuing with reference to FIG. 1, endpoint system 104 comprises anyprocessing system capable of interconnecting with host system 102, forexample: a laptop computer, personal computer, server system, enterprisesystem, personal digital assistant, cellular telephone, ‘smart’telephone or other personal device, or any other processing systemcapable of remotely accessing host system 102 for the purpose ofaccessing the resources available there on. Endpoint system 104 is seento include, in a conventional manner, a processor and user &communications interface 104A, as well as conventional storagecomponents 104B, operating systems and software (typically contained instorage and operated by the processor) and other conventionalcomponents. Further contained within host 104 are a plurality ofconditions 104F. These conditions are monitored by agents 104E, theagents collecting and transmitting information to agent managers 104Dfor aggregation by agent monitor 104F. The various conditions, as wellas the agent functions, are described in detail herein below.

Considering now the details of policy management system 106, in theillustrated embodiment, the system comprises a conventional processingsystem, for example a server computer, enterprise computer, personalcomputer or a notebook computer. Accordingly, the system is seen toinclude, in a conventional manner, a processor and user & communicationsinterface 106A, as well as conventional storage components 106B,operating systems and software (typically contained in storage andoperated by the processor) and other conventional components. Inaccordance with the present invention, policy management system 106 isseen to include a compliance analysis engine 106C as well as variouspolicy information stored within storage system 106B. As will be seenfrom the description below, compliance analysis engine 106C, typicallycomprising software in data store 106B running on hardware 106A,functions to receive system condition information and process thatcondition information in accordance with the security policies, such asare stored within data storage 106B, in order to generate securityrules. Analysis engine 106C can comprise a portion of the capacity ofprocessor 106A and/or one or more dedicated and/or shared separateprocessor(s).

In accordance with a feature of the present invention, the policy datastored within data store 106B can contain multiple sets of policy datafor use by different endpoint systems 104, for use by different hostsystems 102 and for use by the policy management system 106 itself.

In various embodiments as described in further detail below, thefunctions incorporated and described with respect to policy managementsystem 106 may be contained i) within endpoint system 104, ii) withinhost system 102, iii) as a stand-alone network device otherwiseconnected to network 108, and/or iv) distributed in various combinationsof the foregoing. See, for example, FIG. 5 wherein a compliance analysisengine 106C is shown in each of endpoint systems 104 (engine 106C′) andhost system 102 (engine 106C″). It will be understood that the variousother features of policy management system 106 may be performed by theexisting components of the endpoint and host systems, or otherwiseduplicated, replicated, or omitted within those systems as required toperform the appropriate functions as described herein. Further, as usedhere in, references to the policy management system includes whereappropriate only those components and functions necessary to perform thedescribed functions.

In other embodiments of the invention, host system 102 is used tocontrol access to a network, for example a private network. In one suchembodiment, host 102 comprises a gateway or other type of access controlsystem to a network such as a private network. In another suchembodiment, host 102 functions to make compliance and access assessmentsin accordance with the present invention, and forwards the results ofsuch assessments to another access controller. In such instances, thepresent invention is used to control access by an endpoint such asendpoint system 104, to a network, limiting or permitting endpointsystem 104 to access specific network resources based on its currentlevel of compliance.

As described herein, the various subsystems, agents, processes andmanagers can be implemented using hardware components, softwarecomponents and/or combinations thereof.

With reference now to FIG. 2, there is shown a process 200 in accordancewith the present invention for controlling the access of a user such asendpoint system to a computing resource. As noted above, the presentinvention may be used to control access between different systems suchas an endpoint system and a host system, or within a system, such as toparticular resources available within the system.

Establishing Agents & Managers

As used here in, and generally in accordance with the accepteddefinition in the art, “agents” operate to determine the status ofparticular conditions in a system, as described herein the host system102 and endpoint system 104. It will be understood by the reader thatthe invention is equally applicable to controlling access to resourceswithin a single system as to between systems. For purposes ofexplanation, the invention will be described with respect to controllingthe access of endpoint system 104 to host system 102. However, asdescribed above, the invention is equally applicable to controllingaccess within host system 102 and/or endpoint system 104, as well asother computing systems.

Again, as is generally in accordance with the accepted definitions inthe art, an “agent manager” operates to control the function of as wellas to aggregate data collected by the various agents. An “agent monitor”functions to aggregate the data collected by various agent managers. Thevarious agents, managers and monitors can be implemented in hardware,software, and/or combinations thereof.

When used to describe the operation of an agent, the terms “state,”“condition” and variants thereof are used synonymously to describe thestatus of the agent.

Considering first the selection of conditions to monitor within endpoint104 (step 202), there are many different data sources and data elementsthat can be examined to assess the state of the endpoint, formcompliance assessments, and ultimately make policy-based access controldecisions regarding local and remote computing resources.

Individual configuration data elements such as antivirus heuristicsscanning status, and state data elements such as ‘is antivirus currentlyoperating’, can be obtained by establishing an interface to an agentspecifically designed to collect and report that piece of information.Such configuration states and data elements are indicated in the drawingFIG. 1 as conditions 104F. The agents 104E can comprise a component ofthe endpoint system or an external service provided by third partysoftware. The endpoint system includes one or more agent managers 104D.These agent managers collect state information from individual agents104E or the general computing environment, including the operatingsystem version, registry settings, and others as will now be apparent tothe reader. An agent monitor 104C functions to collect and processinformation from the various agent managers 104D, in the mannerdescribed below.

A given inspection agent may provide a granular or broad means toindirectly assess configuration state and data elements and may providenumerous pieces of state configuration and state information to theendpoint's agent managers 104D. For example the response to a queryregarding the state of a configuration setting might simply be true orfalse, whereas the response to a query regarding what viruses arecurrently being monitored for could be an enumerated list of thousandsof virus names.

Agents running on endpoint 104 and performing related or similarfunctions can generally be grouped into categories. For example, anantivirus client/agent, an anti-spyware agent, a content filtering agentand an applications white-list agent can be grouped into a ‘securityagent’ category.

It will be understood by the reader that the universe of monitorableconditions, sources of state information, will expand and evolve overtime. For example, new operating system services may come available, newcategories of security applications may emerge, security point solutionsmay become integrated, transport technologies will continue to evolve,transport hardware will evolve, features of security point solutionswill evolve, etc. Therefore the present invention contemplates theaddition, modification, or removal of agent components as needed overtime. Furthermore, different customer needs will warrant monitoring orconversely not warrant monitoring of selected conditions. The presentinvention is extensible to be able to take advantage of new sources ofinformation as they become commercially available or as customersrequest support for new or existing products. The agents, managers andpolicies are also desirably flexible since, depending on the presence orabsence of operating system facilities, third party applications, etc.not all condition information may be available simultaneously. Thereforeendpoint 104 is configured so as to be able to add, modify, or removeagents on a per user basis and to further customize or adapt a givenconfiguration of the endpoint's software components over time.

Illustrative conditions 104F that are available and may be used forassessing endpoint state information are as follows. Note that not allof these conditions will be needed at any one point in time, i.e. whendifferent system events occur, different pieces of endpoint stateinformation become relevant. It will be understood that different itemsof interest may be monitored at different times, and different userswill have different items they are interested in monitoring.

User state information includes:

-   User ID, User group(s) membership (e.g. reseller, customer, business    unit, division, department, etc.),-   User role(s)/position (e.g. sales, executive, clerical worker,    mobile professional, system administrator, etc.),-   User workgroup, and-   User security group.

Authentication state information includes:

-   Authentication method (e.g. no authentication, reusable password,    one time password, biometrics, smart card, etc.),-   Authentication source (E.g. local to the machine or to a remote    authentication database across a network),-   Authentication success/failure result, Password strength, Age of    password, and Number of successive login failures.

Endpoint hardware Information includes:

-   -   Endpoint hardware owner (public kiosk, user-owned, corporate        asset, etc.)    -   Endpoint hostname    -   Hardware configuration and state, such as:        -   CPU type        -   Total system memory        -   Free memory        -   Etc.    -   BIOS:        -   Vendor        -   Version        -   Individual settings    -   Drive mappings    -   Supported pointing devices    -   Enabled and active pointing devices    -   Current power source    -   Battery charge level    -   System temperature

Endpoint Operating System Information includes:

-   -   Base OS version    -   Installed service packs    -   Installed patches    -   State of OS configuration settings (enabled/disabled options,        services settings, option settings, etc.)    -   Currently active OS services    -   Default language    -   Installed language packs

Operating System Services Information includes:

-   -   Intra-application and Internet-application copy/paste service        (e.g. Microsoft Windows Clipboard)

Network Services Information includes:

-   -   DNS:        -   Current primary and secondary DNS servers        -   Size of DNS cache        -   Number of DNS queries        -   DNS queries serviced by local DNS cache    -   ICMP:        -   ICMP messages transmitted        -   ICMP messages received    -   ARP:        -   Contents of ARP cache        -   Number of ARP requests        -   Number of RARP requests    -   Network protocols enabled    -   IP settings:        -   Current TTL setting for outbound IP packets        -   IP address        -   Default gateway        -   Subnet mask    -   UDP/TCP        -   Window size    -   HTPP:        -   HTTP requests sent        -   HTTP request transmission rate        -   Number of requests to a given host        -   Number of requests to a given domain

Number of requests to a given IP address or address rangeFile SystemInformation includes:

-   -   Read/write status of a named file    -   Access privileges to a named file    -   Access privileges to a named folder or directory.    -   File being deleted    -   File being created    -   File being opened    -   File being overwritten

Application Information includes:

-   -   Installed application information        -   Vendor        -   Version        -   Configuration settings        -   License ID        -   Digital signature    -   Running applications    -   Running processes    -   Current priority level for each running application    -   Application being opened    -   Application being closed    -   Memory consumed by each running application    -   Application update history information    -   Preferred application priority    -   Number of times an application is opened, per hour, per day, per        week, per month, etc.    -   Transaction response time for specific application transactions    -   Number of times a specific application transaction occurs

Application-Specific Information includes:

-   -   Email:        -   Version in use        -   Max number of emails per minute        -   Number of emails received and in inbox or other mail folders        -   Email arrival rate        -   Email reception rate        -   Email attachment count        -   Email attachment size        -   Number of recipients in emails sent    -   Web browser:        -   URLs being accessed

Data Information includes:

-   -   Local data being accessed    -   Application accessing the local data    -   Remote data store being accessed    -   Application accessing the remote data    -   Remote data elements be accessed    -   User access privileges for data being accessed    -   Data being copied or saved to a local external storage device        (e.g. USB thumb drive)    -   Data being transmitted to, copied to, or saved to a remote        location    -   Remote location data being transmitted to    -   Remote location data being retrieved from    -   Specific text strings (including support for wildcards and        logical AND/OR/ELSE/NOT combinations) contained in a file, in a        document, in an email, in a communications message, etc.

Data Backup Information includes:

-   -   Backup program information        -   Vendor        -   Version    -   Backup configuration settings:        -   Specific data to be backed up (e.g. files, folders, modified            documents, tables, records, etc.)        -   Backup type (e.g. incremental, whole)        -   Backup destination    -   Amount or volume of data to be backed up    -   Date of last backup    -   Date of next backup    -   Backup agent state (e.g. active, idle)

Antivirus Agent Information includes:

-   -   Antivirus agent information        -   Vendor        -   Version        -   Signature files version    -   Antivirus-specific configuration settings, (e.g. scan whole        system, specific folders, specific files, run scan at startup,        run scan every X days, signatures update frequency, etc.)    -   Amount or volume of data to be scanned    -   Date of last update    -   Antivirus scanning state (e.g. active, idle)

Personal Firewall Agent Information includes:

-   -   Personal firewall agent information        -   Vendor        -   Version    -   Personal firewall-specific configuration settings (e.g. user        notify, silently discard, event logging, event log uploads,        blocking enabled/disabled, etc.)    -   Permitted/Restricted outbound applications, protocols and/or        destinations    -   Permitted/Restricted inbound applications, protocols and/or        destinations    -   Date of last software update    -   Date of last profile update    -   Personal firewall state (e.g. actively blocking, blocking        disabled, etc.)

VPN Client Information includes:

-   -   VPN client program information        -   Vendor        -   Version    -   VPN client-specific configuration settings (e.g. default        profile, split tunneling, authentication method, etc.)    -   Date of last software update    -   Date of last profile update    -   VPN tunnel state (e.g. connecting, connected, disconnecting,        disconnected)

Anti-Spyware Agent Information includes:

-   -   Anti-spyware agent information        -   Vendor        -   Version        -   Signature files version    -   Anti-spyware-specific configuration settings, (e.g. scan, whole        system, specific folders, specific files, run scan at startup,        run scan every X days, signatures update frequency, etc.)    -   Date of last update    -   Anti-spyware agent scanning state, (e.g. active, idle)

Data Encryption Agent Information includes:

-   -   Data encryption agent information        -   Vendor        -   Version    -   Data encryption-specific configuration settings        -   Method of user authentication        -   Specific data to be encrypted (e.g. files, folders, modified            documents, tables, records, etc.)        -   Encryption type (e.g. AES, digital certificate, TPM chip,            etc.)    -   Data encryption agent state, (e.g. active, idle)

Content Filtering Agent Information includes:

-   -   Content filtering agent information        -   Vendor        -   Software version        -   Blocked sites file version    -   Content filtering agent-specific configuration settings        -   Method of filtering (e.g. local list, proxy server)        -   Specific sites or site categories to be filtered        -   Event logging        -   Log upload    -   Content filtering agent state, (e.g. active, idle)    -   Date of last software update    -   Date of last filter list update    -   Local HTTP/HTTPS proxy settings for remote HTTP/HTTPS proxy        server

Asset Management Agent Information includes:

-   -   Asset management agent information        -   Vendor        -   Software version        -   Asset reporting profile version    -   Asset management agent-specific configuration settings        -   Information being recorded        -   Log upload destination server    -   Asset management agent state, (e.g. active, idle)    -   Date of last software update    -   Date of last profile update

Location Information includes:

-   -   Geographic location    -   Physical location on the corporate campus    -   Location category:        -   Directly connected to corporate network        -   Home        -   Public wireless location        -   Hotel        -   Approved kiosk        -   Public wired broadband location    -   Remote and connected to corporate network via a VPN    -   Reachability of specific remote hosts or networks

Time-Based Information includes:

-   -   Local time of day    -   Time of day at destination    -   Day of week    -   Day of month

Wireless Connection Information includes:

-   -   Permitted SSIDs    -   Prohibited SSIDs    -   Suspect SSIDs    -   Configuration of current wireless connections, e.g.        -   Bluetooth:            -   Current connection details            -   Permitted connections configuration settings        -   Wi-Fi and other IEEE 802.1 wireless data communication link            protocols            -   Current connection details, e.g. ad hoc mode, network                node, WEP, WPA, WPA2, 802.1x, key length, etc.            -   Permitted connections configuration settings

Available Connection Information includes:

-   -   Available network connections    -   Specific network devices available (specific adapter or modem in        use)    -   Network technologies available (Wi-Fi, wired, mobile data, dial,        etc.)    -   Theoretical bandwidth available    -   Cost per minute/cost per megabyte    -   Network service provider    -   Link encryption options

Active connection information includes:

-   -   Specific network device in use    -   Network technology in use    -   Theoretical available bandwidth    -   Current bandwidth    -   Average bytes/sec output    -   Average bytes/sec input    -   Cost per minute/cost per megabyte    -   Network service provider    -   Network printing status    -   Link encryption method    -   Authentication method    -   Network bytes received    -   Network bytes transmitted

Subsequent to identifying the various conditions to be monitored withinendpoint system 104 (step 202), the various agents 104E and agentmanagers 104D and agent monitor(s) 104C are identified and configuredfor monitoring those various conditions (step 204). For example an agentmanager 104D may be configured to query a vendor-specific API exposed bya third party antivirus agent, may be configured to query an operatingsystem service periodically to determine if the endpoint has an activenetwork interface and if so, the IP address of that interface, etc.Multiple managers 104D may be separately configured to monitor multipleagents 104E and multiple monitors 104C configured to aggregate managerdata. In summary, agent managers are configured to monitor theconditions of interest such as one or more of those described above.

Agents can be free standing external software applications, systemservices provided by the operating system or dedicated, special-purposemonitoring processes that are part of the monitored system itself.Agents can monitor both software activity and hardware activity. Atypical method for monitoring hardware information is through the use ofhardware device drivers and other similar operating system services.Examples of freestanding agents are antivirus client, personal firewall,anti-spyware, anti-phishing agents, data backup agents, etc. Agentmonitor 104C can comprise software, hardware and/or a combinationthereof, and is functional to collect or aggregate the input from thevarious agents, through the agent managers, and communicate that datafor processing as described herein.

With reference now to FIG. 3, there is illustrated diagrammatically anexemplary series of agents 104E connected to monitor exemplary endpointconditions 104F such as those listed above. The agent monitors 104Cperform overall endpoint monitoring through the use of individual agentmangers 104D, each of which monitors one or more specific agents 104E,the individual agent managers 104D aggregated by an agent managementservice 104D′. As previously mentioned, different configurations andpolicies will require the use of different individual agent managers anddifferent specific agents. Further illustrated in FIG. 3 is thecommunication of the agent data to the compliance analysis engine 106Cfor processing in accordance with the methods described herein below.

Establish Rules & Policies

With reference now back to FIG. 2, subsequent to the identification ofthe conditions to be monitored and the establishment of the variousagents, agent managers and agent monitors as described above, there arenext established rules and policies for controlling the access to localresources on the endpoint system 104 or remote host system 102 (step206).

The policies established to control access to host system 102 and/oraccess to local host resources 102G as described above, can specify anumber of behavioral options for endpoint system 104. These policies aretypically established by the operator of host system 102 or theadministrator of endpoint system 104, and stored in the policies storagesection 106B of policy management system 106. Configuration policiesspecify a number of behavioral options for the client. As described herein, configuration policies include both the configurable behaviors ofthe compliance analysis engine and the security policies of the systems.Configuration policy behaviors supported by the client include:

Endpoint inspection management policies, including:

-   -   Enumerated list of endpoint data categories to monitor or not        monitor    -   Enumerated list of sensors within each category to monitor or        not monitor    -   Method of monitoring for each sensor (e.g. active polling, or        passive receipt of events)    -   Frequency of monitoring for each sensor    -   Enumerated list of data elements to be sampled with the        following parameters identified for each sampled:        -   Whether sampling is to occur a regular basis, or whether it            is to be initiated as a result of a system event            -   If sampling is to be initiated in response to a system                event:                -   The event (e.g. an application being launched, a                    network connection being established, a user opening                    a file, a system login event, an application login                    event, an antivirus agent compliance violation,                    etc.)                -   If applicable, a threshold value and type (e.g. 5                    times a minute when antivirus compliance score is                    below 75%, email transmission rates above 5 per                    minute, etc.)        -   Number of samples to collect for a compliance evaluation            cycle        -   Sampling interval (if applicable)        -   Acceleration window interval (if applicable)        -   Whether sampling and results reporting method should utilize            a successive stop/start windowing method or a sliding window            method (e.g. for moving average-type calculations).    -   Enumerated list of data elements to be sampled on a regular or        threshold basis, and the corresponding sampling interval    -   Enumerated list of policies and thresholds for which the        sampling frequency must be adjusted when a threshold is reached.        For each policy one or more of the following parameters must be        defined:        -   Threshold value        -   Upper and lower threshold value (for range-based thresholds)        -   Sampling parameters (e.g. count, interval, etc.) when out of            range        -   Sampling parameters (e.g. count, interval, etc.) when in            range

Compliance Engine Management, including:

-   -   Enumerated list of analytical model(s) to use for different        endpoint data elements        -   Business rules        -   Boolean tables        -   Matrix method 1        -   Matrix method 2        -   Mean method        -   Moving average method        -   Variance method        -   Standard deviation method        -   etc.    -   Enumerated list of compliance thresholds for different endpoint        data elements:        -   Min value        -   Max value        -   Required range (min and max value)        -   Variance        -   Standard deviation    -   Composite scoring inputs:        -   Mandatory inputs        -   Exception based        -   Combined            -   Enumerated list of items that are mandatory inputs into                the composite score            -   Enumerated list of items that are exception inputs into                the composite score    -   Composite scoring calculation method:        -   Discrete        -   Time base            -   Sampling interval            -   Number of samples    -   Hostname of remote computer management application to which        endpoint information should be sent    -   Type of information to send to management application, e.g. raw        collected data, compliance analysis results, compliance actions        scheduled to occur, etc.    -   Frequency with which client should query policy management        server to look for and retrieve any available policy updates.

Action Management information including:

-   -   Enumerated list of action categories to enforce or not enforce    -   Enumerated list of actions within each action category to        enforce or not enforce

Enumerated State Policies information including:

-   Endpoint Hardware Configuration Policies    -   Permitted devices types    -   Required device manufacturer    -   Required device version    -   Minimum free hard drive space    -   Required device serial number    -   Required device asset tag    -   Removable storage device permissions    -   Required operating system version    -   Required operating system patches    -   Required operating system configuration settings    -   Permitted operating system configuration settings

Endpoint Data Storage Device Access Policies information including:

-   -   Prerequisites for a named I/O port or storage device to be        permitted to be accessed as read only    -   Prerequisites for a named I/O port or storage device to be        permitted to be accessed as read/write only    -   Prerequisites for a named I/O port or storage device to be        permitted to be accessed as write only    -   Enumerated list of applications permitted to access named I/O        ports or storage devices

Printer Access Storage Policies

-   -   Prerequisites for a named printer to be permitted to be used    -   Named applications allowed to access named printers

Authentication Policies

-   -   Password reset age or date    -   Password expiration age or date    -   Required user location to allow password reset activation    -   Permitted authentication methods for system access    -   Permitted users to be logged into this endpoint

Application Policies

-   -   Permitted applications per named user    -   Permitted application versions per named user    -   Permitted applications for a specified endpoint hardware        configuration    -   Permitted transactions per named application per named user    -   Prerequisites for a named application to be permitted to run    -   Endpoint state conditions that require a named application to be        exited immediately.    -   Applications to automatically uninstall upon detection    -   Applications to automatically uninstall if usage falls below a        specified threshold of use (e.g. number of times opened or used        per day, per week, per month, etc.)    -   Default OS priority level when running    -   Preferred OS priority level when average CPU utilization exceeds        threshold    -   Application priorities when average CPU utilization exceeds        threshold    -   Application priorities when instant CPU utilization exceeds        threshold    -   Minimum free memory requirements to be permitted to run a        specified application    -   Cumulative frequency thresholds for named transactions (e.g. 90%        of all new order upload transactions must complete within 5        seconds)    -   Enumerated list of applications to back up.        -   Preconditions/prerequisites for initiating backup, e.g.            -   When user is connected to corporate network via a VPN                AND            -   User has a wired broadband connection OR            -   User has a Wi-Fi connection    -   Required operating system patches to run a specific application    -   Required operating system configuration settings to run a        specific application    -   Required HTTP/HTTPS proxy settings

Data Access Policies

-   -   Local data permitted to be accessed    -   Local data permitted to be modified    -   Remote data permitted to be accessed    -   Remote data permitted to be modified    -   Local files permitted to be deleted    -   Remote files permitted to be deleted    -   Data permitted to be transmitted to remote locations    -   Remote locations data permitted to be transmitted to; Enumerated        for each file, folder and/or file type    -   Required security posture to have read or read/write privileges        to specific data    -   Local data permitted to be accessed by authentication method    -   Remote data permitted to be accessed by authentication method    -   Local data permitted to be modified by authentication method    -   Remote data permitted to be modified by authentication method    -   Data permitted to be transmitted to remote locations by        authentication method    -   Data permitted to be transmitted to remote locations by link        encryption method

Data Backup Policies

-   Enumerated list of folders and/or files to back up.    -   Preconditions/prerequisites for initiating backup        -   Example:            -   When user is connected to corporate network via a VPN                AND            -   User has a wired broadband connection OR            -   User has a Wi-Fi connection        -   Example:            -   Initiate incremental backup when:                -   When user authentication fails 3 successive times                    AND                -   Wired broadband network connection exists OR Wi-Fi                    network connection exists            -   Initiate full backup when:                -   When user authentication fails 3 successive times                    AND                -   Network connectivity exists over any transport type            -   Initiate full backup when:                -   User is connected directly to corporate network OR                    User is remotely connected to corporate network via                    a VPN AND                -   User authentication fails OR User access privileges                    have been revoked-   Maximum number of days, or hours between data backups for    incremental backups-   Maximum number of days, or hours between data backups for full    backups-   Data to be backed up in incremental back ups-   Data to be backed up in full back ups-   Data to be backed up when not attached to corporate network-   Data to be backed up when connected via a VPN to corporate network    over a specified transport-   Data to be backed up by specified link encryption method

Endpoint Location Policies

-   -   Permitted remote locations    -   Permitted corporate office locations

Authentication Policies

-   -   Permitted authentication methods    -   Max number of days between password resets    -   Max number of authentication failures

Network Access Policies

-   -   Permitted network addresses and/or address ranges allowed to be        accessed by the user    -   Permitted network addresses and/or address ranges allowed to be        accessed by a specific named application    -   Required applications to be running in order to enable a        specified network adapter    -   Required applications to be running in order to enable a        specified modem    -   Permitted network transports    -   Permitted network devices    -   Permitted network service providers    -   Permitted hotspots    -   Permitted dial numbers    -   Permitted wired broadband locations    -   Permitted link encryption options by transport    -   Cost per minute limit    -   Cost per megabyte limit    -   Permitted authentication methods    -   Maximum connection duration by transport    -   Maximum bandwidth consumption by transport    -   Days of week network connectivity permitted    -   Time of day network connectivity permitted    -   Permit local application X, Y and Z to have network access        -   When antivirus is running AND        -   When personal firewall is running AND        -   Antivirus vendor is Symantec AND        -   Antivirus version is v5 or greater

CPU Utilization Policies

-   -   CPU utilization threshold for triggering application        prioritization adjustments    -   CPU sampling interval    -   CPU sampling window    -   Sampling method (fixed interval, moving average, combined, etc.)    -   Enumerated list of applications to disable if instant CPU        utilization threshold exceeded    -   Enumerated list of applications to have operating system        priority levels forcibly changed if instant CPU utilization        threshold exceeded    -   Enumerated list of applications to disable if average CPU        utilization threshold exceeded    -   Enumerated list of applications to have operating system        priority levels forcibly changed if average CPU utilization        threshold exceeded    -   CPU increase rate

Application-Specific Policies

-   -   Email:        -   Permitted and/or restricted source email addresses or            domains        -   Permitted and/or restricted destination email addresses or            domains        -   Maximum number of outbound emails per minute        -   Maximum number of inbound emails per minute        -   Permitted recipients when email contains a specific text            string (support for wildcards and logical combinations of            AND, OR, NOT, ELSE, IF, etc. is supported)        -   Rate of outbound emails    -   Web browsers        -   Permitted URLs or domains        -   Restricted URLs or domains        -   Permitted web sites/content        -   Prohibited web sites/content

File System Policies

-   -   Files to automatically delete upon detection    -   Format disk policies, e.g.:        -   When incremental or full backup has occurred within the last            e.g. 72 hours AND        -   User fails authentication 5 successive times    -   File protection policies, e.g.:        -   Set data files to read only when e.g.            -   When antivirus is not running OR            -   When antivirus reports an infected system

Antivirus Policies

-   -   Permitted vendor(s)    -   Permitted product name(s)    -   Permitted version(s) for named vendors    -   Max permitted antivirus update age    -   Required antivirus product    -   Required antivirus version    -   Required antivirus configuration settings    -   Required antivirus runtime status    -   Required virus definition files minimum version    -   Required frequency of updates to virus definition files    -   Enumerated list of virus threats with attack type and severity        level identified for each

Personal Firewall Policies

-   -   Required firewall product    -   Required firewall version    -   Required firewall configuration settings    -   Required firewall runtime status

Anti-Spyware Policies

-   -   Required anti-spyware agent product    -   Required anti-spyware agent version    -   Required anti-spyware agent configuration settings    -   Required anti-spyware agent runtime status    -   Required anti-spyware signature files minimum version    -   Required frequency of updates to anti-spyware definition files    -   Enumerated list of spyware threats with attack type and severity        level identified for each

Endpoint Patch Management Policies

-   -   Required patch management agent product    -   Required patch management agent version    -   Required patch management agent configuration settings    -   Required patch management agent runtime status    -   Required frequency of updates to patch management definition        files

The solution provides the ability to add support for additional policiesin the future.

Wireless Signals Policies

-   -   Minimum signal strength to connect to Wi-Fi transport    -   Minimum signal strength to connect to CDMA EV-DO transport    -   Minimum signal strength to connect to CDMA 1× RTT transport    -   Minimum signal strength to connect to GSM transport    -   Minimum signal strength to connect to GPRS transport    -   Minimum signal strength to connect to EDGE transport    -   Minimum relative signal strength    -   Permitted wireless network connectivity modes, e.g. Wi-Fi ad hoc        mode, Wi-Fi infrastructure mode, 802.1x authentication required,        802.1x authentication type

Active Network Connections

-   -   Minimum average bytes out/sec threshold

Administrator Policy Configuration

The invention includes a graphical user interface application accessablethrough 106A that allows an administrator to: view available options forendpoint inspection using centralized policy management system 106, viewcompliance policies and policy enforcement actions, specify the policiesof interest to them, and specify specific values for each policy ofinterest. All changes made by the administrator are saved to the policydatabase 106B and made available for all endpoint systems 104 or hostsystems 102 in the policy group to which those policy settings apply.Alternatively, this functionality could be included in a graphical userinterface application on the endpoint system 104 or a graphical userinterface application on the host system 102, when users or localadministrators of those computing devices are responsible forconfiguring their own policy settings locally.

One additional function of the policy management system 106 is theability to receive and respond to policy update requests from endpoints104 and hosts 102. The endpoint system 104 and/or host system 102 areconfigured via a policy setting to periodically query one or more remotepolicy database(s) 106B residing on the policy management system 106 andretrieve updated information about new policies and updated policysettings. The processor then stores this information in a local datarepository.

Because the number of policy options can be daunting, the policymanagement system user interface 106A can provide a control that allowsan administrator to effectively summarize on a sliding scale, e.g. 1-5,High/Medium/Low, 1-100, etc. their desired security posture, orconversely their security posture noncompliance tolerance. A set of datatables in the policy management database maps each setting on thissliding scale to the enablement and/or disablement of specific policiesand policy actions, as well as specific compliance thresholds or scores.This greatly simplifies the administrator's task when establishing andconfiguring policies. A ‘Custom’ or comparable user interface control isalso made available that allows an administrator to bypass the summarycontrol and directly access the complete set of granular policysettings. The values in the data tables used to map a summary securitylevel to specific policies and compliance thresholds are of course ableto be changed by the database administrator at any time.

Integration with Vulnerability Scoring Systems

Many computer hardware and software vendors are known to maintain arunning list of known security vulnerabilities in their products. Seefor example vendor Web sites:

-   www.microsoft.com/technet/security/alerts/matrix.mspx-   www.cisco.com/en/US/products/products_security_advisories_listing.html    As used herein, references to software and software programs to    describe a security vulnerability are to be interpreted in their    broadest sense, including software such as application programs,    operating systems and drivers, combinations of software and hardware    and hardware.

Because each vendor has their own terminology, definitions andsubjective view of what constitutes a vulnerability and the degree ofrisk or exposure a given vulnerability represents (i.e. its severity),there are several industry initiatives to standardize vulnerabilitydefinitions and scores. See for example:

-   www.kb.cert.org/vuls-   www.first.org/cvss/cvss-guide.html

Depending on the source, information that may be published about eachvulnerability includes information such as descriptive parameters thatdescribe the hardware or software at risk (e.g. Intel-based hardwarerunning Windows XP Service Pack 2), possible system impacts (e.g. memorybuffer overflow, unauthorized remote control of the computer, etc),severity type, severity level, sources of more information, datevulnerability was first reported, etc.

Vendors often use this information to prioritize their responses tovulnerabilities in their products. Responses typically take the form ofcustomer notifications, often accompanied by specific interim remedialactions to take (e.g. disable a service, shut down a TCP port, etc.)and/or information on currently available patches that can be applied toeliminate the vulnerability.

When there is no current software available to eliminate thevulnerability, the vendor will normally begin scheduling internalactivities to develop a solution to the vulnerability and make thesolution available to customers and product users as a ‘patch’ or‘update’. Once this becomes available, customers may receivenotification, and/or find notification information on a vendor's website.

Information technology (IT) managers, also referred to herein asadministrators, access vulnerability information by either receiving anotification from a vendor or industry group, going to the vendor orindustry web site and querying the vulnerability database, or byestablishing an electronic communications link with the remote databaseand electronically receiving vulnerability database updates on aperiodic basis. IT managers typically use a combination of industry riskassessment and vendor risk assessment information to prioritize whichvulnerabilities and patches to focus on first, and to prioritizeremediation activities relative to other routine IT operating activitiesand other IT projects.

There is often a significant gap of several days to several monthsbetween when a vulnerability is announced by a vendor or an industrywatchdog and when the vendor releases a patch or update that addressesthat vulnerability. In addition, there is an inevitable gap of days,weeks and possibly even months from the time the patch first becomesavailable to the time the IT manager becomes aware that the patch isavailable, retrieves the patch, tests the patch, identifies the endpoints needing the patch and deploys the patch to all end points and/orhosts needing the patch. This is a very dangerous period of time forendpoint security, during which the system is vulnerable to theidentified security risks.

The interval of time between when a vulnerability is announced and whena vulnerable endpoint is patched commonly referred in the IT industry toas an ‘exploit window’, i.e. a window of time in which a security attackthat specifically, opportunistically targets that publicizedvulnerability can be created and used to probe endpoints to findvulnerable ones that can be attacked. During the exploit window, theendpoints remain exposed to a security attack unless some temporarysecuring action is taken to protect the endpoint. Attack exposure may befrom the local machine only, from a remote machine, or both, dependingon the nature of the vulnerability. The attack may utilize only the newexploit or more commonly utilize a combination of exploits to gaincontrol of the system, gain reliable access to the system, take anaction on the local system, or have the local system initiate acommunications session with a remote computer of the hacker's choosing.

Combining information sources such as those described above, it ispossible using the present invention to create a vulnerability policydirectory including but not limited to the following information:Description of hardware and/or software that is vulnerable, descriptiveattributes (e.g. whether it is exploitable locally or remotely, whetherit impacts data confidentiality, data integrity or computing resourceavailability, etc.) specific remedial or corrective actions to take toeliminate the vulnerability (e.g. halt an operating service, block aport, block an application, disable a network interface, etc.), and thevulnerability severity level (e.g. high/medium/low, 4 out of 5, 7.5 outof 10, 65%, etc.). The present invention uses this information inaccordance with the process shown and described with respect to FIG. 6.By using this information to eliminate the vulnerability almostimmediately after it the information is publicly available, the presentinvention is able to provide almost immediate protection for anycomputing device against vulnerability-specific exploits or securityattacks during the period of time between when the security attack iscreated and used, and when the IT manager or end user has received thesoftware patch from the software vendor and applied that samepatch/repair to the computing device. Initially, the security riskinformation is stored on a data repository, for example within policymanagement system 106, that is accessible to remote endpoints viacommunications links, e.g. the Internet (step 602).

In accordance with this embodiment of the invention, the client softwareis configured via a policy setting to periodically query one or moreremote vulnerability policy database(s) and retrieve updated informationabout new vulnerabilities and updated information about existingvulnerabilities (step 604). The client then stores this information in alocal data repository (step 606).

The client software is configured via policy settings to examine eachvulnerability stored in the local data repository on a periodic basis,or whenever a particular system or policy compliance event warrants(step 608). The client software can subsequently utilize thisinformation in one or more of several different ways to diminish thissecurity risk (step 610), depending on how its policy settings areconfigured:

-   -   The client can inspect each entry in the vulnerability        directory, inspect the endpoint to see if the vulnerability is        applicable, and if so, take the corrective action specified.        Such capabilities are readily commercially available today.    -   The client can inspect each entry in the vulnerability        directory, inspect the endpoint to see if the vulnerability is        applicable, and if so, examine the severity level and compare        that to a policy-defined severity level, and corresponding        policy-defined actions to take when a vulnerability with the        specified severity level or a higher severity level is found.        -   If the severity level equals or exceeds a specified            policy-defined value, then take the corrective action            specified.            -   Optionally if enabled via a policy setting, the client                can subsequently inspect the endpoint to determine                whether the corrective action succeeded or the                vulnerable condition still exists.                -   If the corrective action taken does not succeed,                    consider the endpoint out of compliance and take one                    or more policy-defined corrective actions, e.g.                    block access to a file, a folder, an application,                    network connectivity, establishing a VPN tunnel,                    provide a notification to the user, etc.                -   If the corrective action taken does not succeed,                    consider the endpoint out of compliance and adjust                    one or more security compliance scores where                    applicable. The revised scores when fed into the                    compliance analysis engine along with other endpoint                    state data may result in one or more policy-defined                    corrective actions being taken, e.g. block access to                    a file, a folder, an application, network                    connectivity, establishing a VPN tunnel, etc.

The client can inspect the one or more vulnerability characteristicspresent in the collective set of information, such as the access vector,(e.g. is the vulnerability exploitable locally or remotely, does iteffect confidentiality, integrity or availability, etc.) and comparethat to a policy-defined list of characteristics to be on the lookoutfor, and corresponding policy-defined actions to take when avulnerability with the specified characteristic is found:

-   If the vulnerability characteristic matches a policy-defined value,    then take the corrective action specified.    -   Optionally if enabled via a policy setting, the client can        subsequently inspect the endpoint to determine whether the        corrective action succeeded or the vulnerable condition still        exists.    -   If the corrective action taken does not succeed, consider the        endpoint out of compliance and take one or more policy-defined        corrective actions, e.g. block access to a file, a folder, an        application, network connectivity, establishing a VPN tunnel,        etc.    -   If the corrective action taken does not succeed, consider the        endpoint out of compliance and adjust one or more security        compliance scores where applicable. The revised scores when fed        into the compliance analysis engine along with other endpoint        state data may result in one or more policy-defined corrective        actions being taken, e.g. block access to a file, a folder, an        application, network connectivity, establishing a VPN tunnel,        etc.

Analyzing Agent-Collected Condition Data

With reference now back to FIG. 2, the various condition data describedabove is collected by the agent managers through the agents (step 208)and then analyzed (step 210). With reference to FIG. 4, there is shownin block diagram format the functional aspects 400 of collecting agentdata from various exemplary agents 104E, collected through variousexemplary agent managers 104D, aggregated by the agent monitoringservice 104C for processing by analysis engine 106C, subsequentlyresulting in one or more actions being taken by various exemplary agents104E.

As shown, and described in further detail herein below, the output ofanalysis engine 106C is a series of actions to take, block and/orpermit, the actions communicated back to the agents through the variousmanagers. The aggregated set of actions is passed to the agentmanagement service as a set of instructions. The agent managementservice parses the instructions, identifies for each instruction theappropriate individual agent manager 104D capable of executing theinstruction and passes selected instructions to the appropriate agentmanager 104D. The agent manager 104D passes the instructions to theparticular agent 104E it relies on to take a particular action. Theactions taken by the various agents 104E, for example the control systemservices, system resources, system hardware, system applications andsystem data, in endpoint system 104 or host system 102, depend on wherethe various security functionalities of the invention are installed

Additionally, the data collected from various exemplary agents 104E andaggregated by the agent monitoring service 104C can be communicated overa data communications network to the policy management system 106 whichcan also process the collected data using the compliance analysis engine106C. There are several alternative embodiments. One embodiment (call itembodiment 1) has all data collected at the end point analyzed by acompliance analysis engine residing on the end point, (whether that endpoint be a laptop or a host system web server). An alternativeembodiment (call it embodiment 2) has all data collected at the endpoint analyzed by a compliance analysis engine residing on the policymanagement server. In this latter embodiment, the question is whathappens when the policy management server completes the complianceanalysis and determines that some policy violations exist and one ormore policy compliance actions must be taken. There are severaldifferent embodiments possible using the policy management server toperform the compliance analysis function (Call these embodiments 2A, 2B,2C, etc. Brief embodiment descriptions follow: Embodiment 2A: Policymanagement server sends policy action instructions (block thisapplication, permit that application, etc.) back to end point forexecution. Note that a best practice would be to digitally sign theinstructions sent to the end point using the policy management server'sdigital certificate. The end point must validate the digital signaturebefore considering the policy action instructions Embodiment 2B: Policymanagement server sends instructions (block this end point, permit thatend point, limit that end point to only host systems residing on the the192.168.10.x subnet, etc.) to a network access control device forexecution. Normally the access control device will as a result of theseinstructions add an Access Control List (ACL) entry to its data trafficforwarding table that subsequently effects what destination host systemsand communication protocols may be used by the end point when the endpoint is trying to reach a host server through the network accesscontrol device. Embodiment 2C: Policy management server sendsinstructions (block this end point, permit that end point, limit thatend point to only the following applications or applicationtransactions) to a host system for execution. Normally the host systemwill as a result of these instructions add an Access Control List (ACL)entry to its session management table that subsequently effects whatapplications or application transactions residing on that host systemmay be accessed or used by the end point when the end point isrequesting services from that host system.

The policy management server creates a list of permitted host systems,applications, and/or application transactions that the end point ispermitted to contact, based on its current degree of compliance. Policymanagement server then digitally signs the ‘permitted actions list’ andreturns the permitted actions list to the end point. When end pointwants to access a host system, the end point presents the digitallysigned permitted actions list to the host system. The host thenvalidates the policy manager's digital signature on the signed permittedactions list and then creates an ACL that allows the end point to accessspecific resources (e.g. files, folders, types of transactions) on thehost system. An alternative and complementary embodiment (Embodiment2D-2) is that when packets from the end point have to pass through anetwork access control device residing between the end point and thehost system, the end point must authenticate to the network accesscontrol device. As part of the authentication process at the networkaccess control device, the end point must present the digitally signedpermitted actions list to the network access control device. The networkaccess control device then validates the policy manager's digitalsignature on the signed permitted actions list and then creates an ACLthat allows the end point to access specific host systems (e.g. a singleor range of IP addresses) and/or to use specific communication protocols(e.g. FTP, HTTP, SMTP, etc). The policy management system 106, shownconnected to the Internet, can be implemented alongside a network accesscontrol device, e.g. a router, switch VPN server, etc. or can remotelycommunicate with the network access control device via a datacommunications network. In this embodiment, the policy management system106 is able to communicate access permission and/or access restrictionsto the network access control device, restricting what host systems 102the endpoint system 104 is able to access, restricting what endpointsystems 104 are able to access host systems 102, and/or restricting whatremote systems host system 102 is able to access. The policy managementsystem 106, when it has received aggregated information from the agentmonitor 104C on endpoint system 104 is also able to send accessinstructions to host system 102 identifying what permissions orrestrictions should be applied to an endpoint system 104 when endpointsystem 104 tries to access host system 102 via the network 108. Notethat this last embodiment does not require the system 104 to have or berunning security-related software such as this invention. Rather, thehost system 102 can be protected and/or restrict access with respect toany endpoint 104 that tries to communicate with it.

Analysis engine 106C (FIG. 1) contains one or more analytical methods ormodels and enables the selection of the optimum model or models for agiven set of conditions 104F as determined by the various agents 104E.In accordance with the present invention, a feature and advantage ofanalysis engine 106C is its support for multiple models, itsextensibility to support future models, and the ability to use multipledifferent models simultaneously either in parallel or in series whileperforming compliance analysis of conditions 104F. The analysis engineanalytical model compares current condition information 104F, policiesregarding those conditions 106B and makes action decisions resultingfrom those conditions and policies, using one or more analytical models.Analysis engine 106C subsequently initiates actions to permit, deny orcontrol access to local and/or remote computing resources based onadditional policies that identified permitted and/or denied actions whena noncompliance condition exists.

Analytical model selections are based on one or more policy-basedconfiguration settings stored in the policy store 106B. These policies,or rules, may alternatively and/or additionally be locally stored on theendpoint system 104 and/or host system 102, accessed by an endpointsystem 104 or a host system 102 from a remote policy management system106 via a data communications network, or a combination of the two. Aswith all other policies, the policy setting controlling what analyticalmodels are used and when they are used can be dynamically changed at anytime by changing the values of the policy settings in accordance withthe processes described above.

The following sections describe some of the analytical models used byanalysis engine 106C. Policy management system 106 is designed to allowanalytical models operated by analysis engine 106C to be added in thefuture, individually upgraded or modified, or removed. Conventionalsoftware distribution methods are used to communicate new or modifiedanalytical models and new versions of the analysis engine 106C. Inaccordance with the present invention, analysis engine 106C is alsoarchitected to allow the inputs and/or actions associated with a givenpolicy to be modified or customized as required. Conventional softwaredistribution methods are used to communicate new or modified policies orpolicy values. Policies incorporating combination rules are alsosupported through the logical combining of multiple individual rulesusing conventional logic clauses such as AND, OR, NOT, ELSE, IF, WHEN,UNLESS, etc.

The analysis engine 106C is the central and primary destination for allcollected or received condition state information collected by the localendpoint system 104. Some or all condition state information to becollected may be requested by the analysis engine on a periodic basis,requested by the analysis engine as a direct result of a detected event,requested by the analysis engine as a direct result of completedanalysis of previously received condition state information, sent to theanalysis engine by agents and agent managers on a periodic basis, and/orsent from agents or agent managers to the analysis engine as a directresult of a detected event. This holds true for instances of localanalysis of condition state information on the endpoint system 104 aswell as remote analysis of condition state information on the policymanagement system 106.

Capabilities of the analysis engine also include the ability to querythe policy data store 106B (FIG. 1) to collect compliance policies andtheir associated value(s). This query could occur on a fixed periodicbasis or be based on a specified system event, for example systemstartup, client startup, application start event, network interfaceevent, authentication event, notification of received policy updates,receipt of a specific endpoint data element, receipt of a specificendpoint data element having a specific value, etc.

Capabilities of the analysis engine further include the ability to querythe policy data store 106B to collect action policies and theirassociated value(s). This query occurs whenever needed by the analysisengine.

Capabilities of the analysis engine further include the ability tooutput status and event messages to local processes or remote computersaccessible across a network. These messages may be used to trigger thedisplay of a message to a user on the local endpoint system 104 userinterface, the display of a message on the policy management system 106,the updating of status information on an already open display or may belogged to a local or remote data store for use in reports.

Endpoint Compliance Assessment Algorithms

With reference now to FIG. 7, there is shown a process 700 for operationby policy management system 106 to determine whether endpoint system 104is in compliance with the compliance policies maintained in data storage106B, the process comprising an expansion of step 210 of FIG. 2. Inaccordance with this process, condition data regarding the status ofconditions 104F are collected through the above described system ofagent managers and monitors, and input into analysis engine 106C throughthe processor and communications interface 106A (step 702). A complianceassessment process, or algorithm, is selected to process the conditiondata (step 704). Many different appropriate algorithms are described andshown herein below. Optionally, as described below, numeric risk valuescan be assigned to non-numeric condition state data and numericweightings applied to numeric values (step 705). The effective andappropriate security policy is retrieved from data storage 106B (step706), the condition data is processed using the selected complianceprocess (step 708), and the results of the processed condition datacompared to the compliance policy (step 710). The details of thisprocess, including the various algorithms, are described in detailherein below.

Because the policy action rules comprise a number of endpoint statesthat must be assessed, because there is a desire to be able to manageand change many policy settings using a finite number of data values andbecause of the number of possible combinations of endpoint states thatcould warrant invocation of the defined action, a simple rules basedapproach to processing this information may be unwieldy and not scalewell. To facilitate the effective practice of the present invention, analgorithmic approach is provided by the present invention. As part ofstep 706 above, the algorithmic approach involves treating thenon-numeric endpoint state information as real time values that areconverted to numerical risk weightings, e.g. 1-100. Non-numeric endpointstate information, listed above, includes those states not communicatedas a number, e.g. is an application running, what level of anti-virusprogram is running, etc.

The policy data store 106B contains a numeric value to assign to eachnon-numeric endpoint condition 104F. When the analysis engine 106Creceives endpoint condition state information 104F from the agentmonitors 104C, the analysis engine 106C makes one or more queries to thepolicy data store 106B for each endpoint condition and retrieves thenumeric value to assign to that particular endpoint condition. Theprocess is repeated as needed for each non-numeric endpoint conditiondata element the analysis engine must convert from a non-numeric valueto a numeric weighting. This process may also be repeated as needed foreach numeric endpoint condition data element the analysis engine mustconvert from a raw numeric value to a normalized numeric weighting, e.g.converting the number of calendar days since antivirus was last updated(e.g. 0-365 days) to a normalized value in the e.g. 0-100 range.

This assignment of numeric weightings to non-numeric states allowseffective analysis of the condition information using a wide range ofnumeric algorithmic models, and further allows non-numeric endpointcondition state information to be included and factored in when theanalysis engine 106C is assessing policy compliance of endpointcondition state information that is already in numeric form. Whatfollows are specific, but not exhaustive examples of specificalgorithmic methods supported by the invention. Other numeric-basedalgorithmic methods within the scope of this invention will becomeapparent to the reader.

Matrix Analysis Algorithm

One analytical model operable by analysis engine 106C involves treatingendpoint condition state information 104F as a matrix of numeric valueswhere as mentioned above and as implied in each of the subsequentanalytical models described herein, the real time state information isconverted to numerical values or risk weightings, e.g. 1-100. Thestandalone and business intelligence rules can be treated as a secondmatrix where rules are given relative importance ratings. By combiningthe two matrices using conventional matrix mathematics, the analysisengine 106C generates a third matrix as the result. This third matrixcontains numerical compliance scores that can be converted to securitycompliance ratings for different enforcement actions. Each rating cansubsequently be compared to a predefined score threshold stored in thepolicy data store 106B for each possible enforcement action to determinewhether or not to invoke the action. If the derived score is above thethreshold, the endpoint is deemed sufficiently (while not necessarilycompletely) compliant with those particular endpoint configurationpolicies.

The security score thresholds, the input matrix elements, the inputmatrix security scores and the items to be included in the endpointinputs list are all data values stored in the policy data store 106B andas such are configurable and extensible so as to allow tailoring to anindividual user's need. Configuration is performed using a userinterface 106A, from which new or revised matrix elements, thresholds,weightings and factors can be created and modified. When implemented ina distributed fashion, changes to these data values made in the policymanagement system 106 can be distributed to the software agent residingon the endpoint system 104 using conventional software distributionmethods. Examples of different matrix analysis methods are shown hereinbelow.

Business Rules-Based Analytical Model for Policy Enforcement

One analytical model operable by analysis engine 106C in accordance withthe present invention utilizes descriptive business rules. The rulesspecify a specific action to take if specified prerequisite conditionsare true. When different system events and policy violations occur,different actions will be initiated. The universe of possible actionswill expand and evolve over time, as will the tests used to determinewhether a given action should be initiated. For example, new operatingsystem services may come available, new categories of security orendpoint management applications may emerge, security point solutionsmay become integrated, transport technologies will continue to evolve,features of security point solutions will evolve, etc. Additionally,different operational needs will warrant creating new actions and newtests. This analytical model is extensible and allows the addition,removal, tailoring, and/or changing the values of prerequisiteconditions or actions for different customers and policy groups. Notethat this rules-based analysis may or may not require the assignment ofnumeric risk scores to non-numeric conditions, depending on the desiredrules.

Examples of business rules used in this analytical model follow.

User Authentication Actions, including:

-   -   Password reset action        -   Force password reset            -   When password age is greater than 90 days AND            -   When user is directly connected to corporate LAN AND            -   No policy violations exist that prevent connectivity to                corporate LAN

Application Access Actions

-   -   Application access block action:        -   Prevent named application from opening            -   When antivirus is out of compliance in any way        -   Prevent named application from opening            -   When antispyware is out of compliance in any way        -   Prevent named application from opening            -   When antivirus is out of compliance in any way AND            -   When personal firewall is not running        -   Prevent named application from opening            -   If user is not connected to corporate LAN        -   Prevent named application from opening            -   If user is not connected to corporate LAN OR            -   If user is not connected from home        -   Prevent named application from opening            -   If any critical OS patches not found AND            -   If user not connected to corporate LAN AND            -   Antivirus not updated within last 212 days        -   Prevent named application from opening            -   If user not connected to corporate LAN AND            -   Day of week is Mon, Tues, Wed, Thurs or Fri AND            -   Time of day is between 8 AM and 8 PM        -   Prevent named application from opening            -   If user not connected to corporate LAN OR            -   User does not have active VPN tunnel        -   Prevent named application from opening            -   If user authentication method anything other than RSA                SecureID    -   Application uninstall actions        -   Uninstall named application if found    -   Application upgrade actions        -   Uninstall named application application if found AND        -   Retrieve named installation package from a named remote            computer AND        -   Initiate installation of named installation package    -   Application upgrade actions        -   If user is connected to corporate LAN AND        -   If approved antivirus client (vendor and version is not            installed) THEN        -   Uninstall named application application if found AND        -   Retrieve named installation package from a named remote            computer AND        -   Initiate installation of named installation package    -   Restrict email application        -   When antivirus reports an infected system OR        -   Anti-spyware agent is not running    -   Restrict HTTP applications such as web browsers        -   When local proxy setting is out of compliance, i.e. not            configured for remote proxy server

File Management Actions

-   -   Protect data:        -   Generate encryption key AND        -   Encrypt a specified file, files, folder or folders AND        -   Transmit encryption key to a policy-defined remote computer.

Hardware Devices Actions

-   -   Launch Lojack application        -   When user fails authentication 100 successive times OR        -   When user attempts to copy encrypted data to USB port AND        -   Network connectivity exists over any transport    -   Disable network adapter        -   When personal firewall is not running AND        -   Antivirus compliance score is less than 75% AND        -   Anti-spyware agent is not running

Network Access Actions

-   -   Disconnect wireless adapter        -   When active wireless connection is ad hoc OR        -   When authentication method is not PEAP and 802.1x

Boolean Table-Based Analytical Model for Policy Enforcement

Another analytical model operable by analysis engine 106C in accordancewith the present invention utilizes a table of Boolean logic rules. Thiswill be understood to be an extension of the business rules-based modeldescribed above, with the inclusion of Boolean logic combinations. Therules specify specific actions to take when specified conditions aretrue. The universe of possible actions will expand and evolve over time,as will the tests used to determine whether a given action should beinitiated. Additionally, different users may prefer different rules, newactions and/or new conditions to determine. This analytical model isextensible both in terms of inputs and actions and allows a user to add,remove, tailor, and/or change the values of inputs and/or actions fordifferent systems.

An example of a policy table containing Boolean logic as used by thisanalytical model follows in Table 1.

TABLE 1 Input 1 Input 2 Input 3 Action 1 Action 2 Action 3 Action 4Antivirus Corporate Required Allow Allow Email Allow Alert Agent NetworkOS Patches Network Application USB IT Running Connection InstalledConnectivity Access Ports Administrator FALSE FALSE FALSE FALSE FALSEFALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUEFALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSEFALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUEFALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE

Scoring-Based Analytical Model for Policy Enforcemen

Other analytical models supported by the present invention utilizedifferent types of mathematical scoring methods. Endpoint stateinformation collected by the agent can be assigned relative importanceweightings or quantitative scores, as described above, to develop acomposite security ‘score’ for the security dimension or dimensionsassociated with that endpoint attribute. The score can subsequently beused as a proxy for a numeric endpoint security health metric for aparticular aspect of the endpoint's configuration or health. Forexample, an antivirus agent monitors the endpoint from a virusprotection dimension and has certain attributes that must be in place toprovide effective antivirus protection. Examples of attributes theantivirus agent must have in order to provide effective end pointsecurity and that is desired to be externally assessable stateinformation to the invention includes:

-   -   The antivirus agent must be running to provide any protection at        all.    -   The antivirus agent must be of a recent version to be able to        recognize certain new virus patterns.    -   The antivirus agent receives periodic virus signature updates        used in the virus scanning and protection process. Frequent        updates, or more precisely a recent update (which is assumed to        have brought the antivirus agent fully up to date) is necessary        to have protection against the latest threats.    -   The antivirus agent has configuration settings that can be        enabled or disabled to provide more or less protection.

Each of these attributes of the antivirus agent can be assigned anabsolute score or a relative weighting by a user, based on the relativeimportance of that particular attribute to that user. For example as isshown in Table 2:

TABLE 2 Agent Attribute Points Weight Antivirus agent active and running60 60% Antivirus agent version current or current minus 15 15% one revAntivirus agent signature files updated within the 100 100% last 212days All antivirus scan options enabled 15 15% Total 100 100%

Different operators may have different views on the relative importanceof these attributes and/or may wish to use different or more granularattributes in their scoring model. For example, a different user maywant to replace the version attribute with a real-time file systemmonitoring enabled attribute or add this as an additional attribute intheir scoring model. Similarly, another user may assign more relativeimportance, hence assign a higher weight or score to how recently theantivirus signature files were updated. Another user might want toassign each of 4 specific configuration settings 5 ‘points’ if thesetting is enabled, for a total of 20 possible points when all antivirusscans options of interest to that user are enabled.

These attributes may be different for different users depending on thecapabilities of their particular endpoint security solution. Forexample, if a particular commercially available antivirus agent has noconfigurable options to enable/disable, this attribute would not berelevant and would not be a consideration in the scoring process. Infact, one of the attributes could easily be the specific product beingused, if a user has high confidence in 1-2 specific antivirus agents andmuch lower confidence in other antivirus agents. Support for variabilityacross different end points having different hardware/softwareconfigurations is managed using policy settings as previously described.

Attributes and weightings can be similarly established for each of theendpoint security agents previously identified. The approach cansimilarly be adapted to other existing and future endpoint securitysolutions using this same approach.

Individual Agent Score Threshold Analysis and Enforcement

By establishing a minimum threshold for an agent and comparing the totalagent score with that threshold, the total score obtained by queryingthe agent and/or its externally viewable attributes can be used as atrigger for one or more general or context-specific predefined actionsto be taken. For example, assuming the following is the list of actionsto be taken if the antivirus agent score does not meet or exceed athreshold of 81 points or 81%:

-   Disable network interfaces so that the endpoint is prevented from    connecting to a network-   Disconnect any active network connections, e.g. a dial, cellular or    Wi-Fi connection-   Provide a user notification of the security state of the endpoint    and instruct them to contact their help desk to resolve the issue.

A different operator may wish to take additional or alternativepredefined actions, for example:

-   Disable any active VPN connections-   Prevent the establishment of a VPN connection-   Apply an outbound access control list on the network protocol stack    or using a personal firewall to limit outbound access to a specified    set or one or more specific application protocols (e.g. HTTP, POP,    etc.), applications (e.g. Internet Explorer web browser, custom    Oracle financial application, Symantec Norton Antivirus Update,    BigFix Endpoint Vulnerability Management agent, etc.), network    addresses (e.g. 192.1068.1.255), network numbers or subnets (e.g.    608.52.1022.0/206) and/or DNS domains (e.g. customer.com,    macafee.net, server1.windowsupdate.com, etc.)    -   Provide a user notification that outbound access is being        restricted to specific applications, networks, etc. as        appropriate as a result of the current security state of the        endpoint.    -   Activate a scripted remediation process to enable the antivirus        agent if not running, update the antivirus signature files,        enable all antivirus configuration settings, etc. as appropriate    -   Once the remediation process is completed, reassess the        antivirus score.        -   If score meets or exceeds threshold:            -   Remove VPN restriction            -   Remove outbound network access restrictions            -   Provide user notification indicating that endpoint                security has now reached a satisfactory state and all                normal system privileges have been restored    -   If score does not meet or exceed the threshold provide a user        notification of the security state of the endpoint and instruct        them to contact their help desk to resolve the issue.

A wide range of alternative system level corrective actions or usernotifications are possible and may be more or less appropriate,depending on the situation and the user's needs. More complexconditional actions including IF, THEN, ELSE, AND, OR type logic mayalso be defined.

Note that in particular, the corrective actions may vary by agent. Thusfor example, the corrective actions when the firewall agent score isbelow the firewall threshold might be:

-   Disconnect any active network connection other than a wired Ethernet    connection-   Prevent any network connections from being established other than a    wired Ethernet connection-   Block outbound network access on the wired Ethernet connection    unless the user's IP address is on the 1020.130.15.x network.-   IF the firewall is not currently running, THEN attempt to restart    the firewall using a predefined command.-   Provide a context-sensitive user notification

Whereas for example the corrective actions when the antivirus agentscore is below the antivirus threshold might be:

-   Permit new network connections to be established-   Permit active network connections to remain active-   Prevent the following named applications from running:    -   Internet Explorer    -   Mozilla    -   Firefox    -   Opera    -   AOL-   Prevent the following file types from being opened:    -   .doc    -   .xls-   Upon detection of an active network connection send an antivirus    update request message to a predefined URL-   Install the downloaded antivirus update package-   Provide user notifications regarding restricted applications-   Provide user status updates during the update process.

Composite Agent Scoring, Threshold Analysis and Enforcement

In the preceding examples, an antivirus agent was the single agent underevaluation. Multiple agents can be simultaneously assessed in a similarfashion and the individual agent scores combined in different ways tocreate a holistic view of the endpoint state from multiple perspectives.For example, a user could define the following agent score combinationlogic as the basis for determining whether the end point is or is not incompliance:

-   Antivirus agent score equal to or greater than 80% AND-   Firewall agent score equal to or greater than 90% AND-   Antispyware agent score equal to or greater than 50%

The individual agents of interest would be periodically queried orassessed at a configurable interval, individual agent scores calculatedand then this business logic applied to determine if a noncomplianceexists and if any predefined corrective, restrictive and/or notificationactions (such as those previously defined) are required.

An alternative approach is to assign relative weighting to theindividual agents, based on their relative importance to the user. Forexample as shown in Table 4 below:

TABLE 4 Relative Relative Agent Points Weight Antivirus agent 15 15%Personal firewall agent 70 70% Antispyware agent 100 100% Contentfiltering agent 5 5% Total 100 100%

The relative weights for individual agents are then combined with theindividual agent scores to derive a composite score. For example, asillustrated in Table 5 below:

TABLE 5 Adjusted Raw score Relative score Agent (points) Weighting(points) Antivirus agent 65 15% 9.75 Personal firewall agent 93 70% 65.1Antispyware agent 0 100% 0 Content filtering agent 100 5% 5 CompositeScore 79.95

In this approach, the composite score is 79.95 points or 79.95%. Thecomposite score would then be compared to a predefined compositethreshold residing as a data value in the policy data store 106B todetermine if any predefined corrective, restrictive and/or notificationactions (such as those previously defined) are required.

Different users may have different views on the relative importance ofindividual agents and may wish to use fewer, additional or differentagents in their composite scoring model. For example, a different usermay want to replace the content filtering agent with a patch managementagent in their composite scoring model or add the patch management agentto the above composite scoring model. Similarly, another user may assignmore or less relative importance, hence assign a higher or lowerrelative weight to the personal firewall. Such differences areaccommodated by the invention through the use of policy settings andvalues that specify the agents of interest, the compliance thresholds,the relative weightings and other relevant considerations.

Complementary Individual & Composite Agent Scoring, Threshold Analysisand Enforcement

While the composite approach provides a comprehensive assessment of theendpoint state and can be the basis for automated notifications orcorrective actions, it does not preclude automated notifications orcorrective actions triggered by assessments of individual agent scores.Therefore composite corrective actions can be defined independently ofindividual agent corrective actions (e.g. antivirus agent correctiveactions, personal firewall corrective actions, etc.) if defined valuesexist in the policy data store 106B. For example, the previous compositeexample can be expanded as follows in Table 6:

TABLE 6 Agent Raw score Threshold Relative Adjusted score Agent (points)(points) Weighting (points) Antivirus agent 65 75 15% 9.75 Personalfirewall 93 90 70% 65.1 agent Antispyware agent 0 70 100% 0 Contentfiltering 100 60 5% 5 agent Composite Score 79.95 Composite 75.00Threshold

In this example, the overall composite score exceeds the compositethreshold, thereby not requiring invocation of previously definedcomposite corrective actions. However, the individual score for theantivirus agent is below the antivirus threshold, thus requiringinvocation of previously defined agent-specific antivirus correctiveactions. Examples of corrective actions were previously defined above.

Single Level Versus Multi-Level Agent Scoring, Threshold Analysis andEnforcement

The previous examples (single agent assessments as well as compositeassessment) all utilized a single threshold. In a single thresholdmodel, when the score is below the threshold, corrective action isrequired and when the score is above the threshold, no correctiveactions are required. This concept is readily extensible (for bothsingle agent assessments and composite assessments) to a multi-levelthreshold model, where different corrective actions exist at differentscore thresholds. Corrective actions to take for different scorethresholds are stored as data values in the policy data store 106B. Forexample:

-   Antivirus Agent Thresholds & Actions    -   40%:        -   Prohibit the following applications from running:            -   Outlook            -   Outlook Express            -   Eudora            -   Thunderbird            -   Cisco VPN client            -   Nortel VPN client            -   Internet Explorer            -   Firefox        -   Block outbound POP protocol traffic        -   Restart antivirus if not running        -   Update virus signature files if greater than 15 days old        -   Enable realtime filesystem monitoring if not currently            enabled        -   Prohibit all .doc and .xls files from opening    -   60%:        -   Prohibit the following applications from running:            -   Cisco VPN client            -   Nortel VPN client        -   Update virus signature files if greater than 15 days old        -   Enable realtime filesystem monitoring if not currently            enabled    -   86%:        -   No restrictions-   Personal Firewall Agent Thresholds & Actions:    -   55%:        -   Restart firewall if not running        -   IF local IP address is 1023.1023.1023.x AND IF endpoint is            able to send ICMP ping to host 1023.1023.1023.56, THEN            permit only wired Ethernet access, ELSE block all outbound            network access on all transports    -   91%:        -   No restrictions-   Composite Assessment Thresholds & Actions    -   51%:        -   Prohibit the following applications from running:            -   Cisco VPN client            -   Nortel VPN client            -   Oracle financials            -   SAP payroll manager        -   Restrict HTTP access to the following domains:            -   Symantec.com            -   Windowsupdate.com            -   BigFix.com            -   Customer.com        -   Block outbound SMB protocol traffic        -   Block write access to the My Documents folder and all            underlying subfolders    -   75%:        -   Prohibit the following applications from running:            -   Cisco VPN client            -   Nortel VPN client    -   85%:        -   No restrictions

Continuous Reporting Versus Exception Reporting Threshold Analysis andEnforcement

In each of the examples above, all collected data points are analyzedfor compliance or given a compliance score that may be examinedindividually or included in a broader composite compliance assessmentprocess. An alternative implementation is to not provide a value to acomposite compliance assessment routine unless there is a complianceviolation and have the composite compliance assessment routine assumethat component is in compliance unless notified otherwise, i.e. utilizeexception-based compliance notifications. In the example previouslydescribed:

Agent Raw score Threshold Relative Adjusted score Sensor (points)(points) Weighting (points) Antivirus agent 65 75 15% 9.75 Personalfirewall 93 90 70% 65.1 agent Antispyware agent 0 70 100% 0 Contentfiltering 100 60 5% 5 agent Composite Score 79.95 Composite 75.00Threshold

The individual raw scores for antivirus, personal firewall, anti-spywareagent, and content filtering must be fed into the composite scoringsoftware process in order for the composite score to be determined.Conversely, in the exception-based model, the composite scoring softwareroutine assumes the individual agent thresholds have been met, (e.g. theantivirus agent score is 75, the personal firewall agent score is 90,the anti-spyware agent score is 70 and the content filtering agent scoreis 60) unless informed otherwise. The exception when reported is used toupdate the composite score data set and a revised composite score iscalculated. This exception-based approach is also supported by theinvention.

Note also that the methods can be combined when so enabled via a policysetting. Continuing with the example above, the composite scoringsoftware routine assumes that the antivirus agent score is 75 points andassumes the personal firewall agent score is assumed to be 90, unlessotherwise notified. However the composite scoring software routine makesno assumption regarding the anti-spyware agent score or the contentfiltering agent score and requires that the antivirus compliance scoringsoftware routine as well as the content filtering compliance scoringsoftware routine both report actual raw compliance scores. Combinationsof this type are also supported by the invention.

Different users may wish to have different sources utilizeexception-based reporting and different sources utilize mandatoryreporting. Such variations and adjustment capabilities are supported bythe invention through the use of policy settings and values residing inthe data store.

Matrix Algebra-Based Analytical Model for Policy Enforcement

Additional analytical models supported by the present invention utilizedifferent matrix algebra methods. This model extends upon the scoringbased analytical model previously described.

Matrix Method #1

In one matrix algebra method, different agents report different types ofinformation regarding the state of the endpoint, such as:

-   Antivirus agent-   Personal firewall-   Anti-spyware agent-   Endpoint vulnerability management-   Content filtering

While these agents monitor and inspect different aspects of the endpointenvironment, from a policy compliance perspective, there are commonpolicies or target states of interest across each of these data sources,such as:

-   -   Whether the agent is running    -   Whether it is a desired or required vendor    -   Whether it is up to date with signature updates    -   Whether it is configured correctly or optimally

Relative weights regarding the importance of compliance for eachattribute can be assigned for each monitored condition. The collectionof information can then be represented in tabular form in anticipationof making the data available for matrix algebra or other linear andnonlinear analysis methods. For example, the following Table 7 shows howone operator has identified 3 data sources of interest, identified 3attributes of interest, and assigned levels of relative importance toeach data source/attribute pairing. These data sources, attributes andvalues are stored in the policy data store. The policy data store alsocontains the specific target values or thresholds for each of theseattributes, e.g. the desired antivirus agent is product XYZ, the maximumage in days of the most recent anti-spyware agent is 30 days, therequired configuration settings and values for the personal firewallare: no inbound access permitted, outbound access using HTTP protocolpermitted, etc.

TABLE 7 Agent From Approved Required Vendor Updated ConfigurationCurrently Within X Settings Data Source Running Days Enabled TotalAntivirus agent 80 15 5 100 Personal firewall agent 60 20 20 100Antispyware agent 70 25 5 100

It will be apparent to the reader that fewer, alternative and/oradditional data sources could be used instead of those shown above.Obviously, fewer, alternative and/or additional attributes could be usedinstead of those shown above.

When the real time or periodic measurement of a given condition is incompliance relative to the security policy, all possible points areawarded. When a given condition is not in compliance relative to thepolicy, no points are awarded.

In the following example:

-   The antivirus agent is running, is from an approved vendor and has    been updated recently. However one or more critical configuration    settings are not set correctly.-   The personal firewall agent is running, is from an approved vendor,    has been updated recently and has configuration settings set    correctly. However it has not been updated in the past 21 days.-   There is no anti-spyware agent running on the endpoint (and possibly    is not even installed on the endpoint).

The resulting matrix that represents the current state of the endpointis as follows:

Agent From Approved Required Vendor Updated Configuration CurrentlyWithin 21 Settings Data Source Running Days Enabled Total Antivirusagent 80 15 0 95 Personal firewall agent 60 20 20 100 Antispyware agent0 25 5 212

As this is an n×n matrix, the matrix determinant can be calculated usingthe following Formula 1:

$\begin{matrix}{{{\begin{matrix}a_{1} & a_{2} & a_{3} \\b_{1} & b_{2} & b_{3} \\c_{1} & c_{2} & c_{3}\end{matrix}} = {{{a_{1}b_{2}c_{3}} - {a_{1}b_{3}c_{2}} - {a_{2}b_{1}c_{3}} + {a_{2}b_{3}c_{1}} + {a_{3}b_{1}c_{2}} - {a_{3}b_{2}c_{1}}} = {- 36}}},500} & {{Formula}\mspace{14mu} 1}\end{matrix}$

The determinant derived from assessing the current state of the endpointcan be compared against a minimum threshold defined in the policy datastore 106B that must be met in order for the endpoint to be consideredin compliance.

Matrix Method #2

The matrix method described above can be further extended by assigningrelative weightings to the data sources, treating the resulting valuesas a row or column vector matrix, and performing matrix multiplicationof the data source relative importance matrix and the current statematrix. This allows the evaluation of compliance in a given dimension orattribute across a number of data sources, factoring in the relativecompliance importance of the different data sources.

For example the following vector shows how the relative weights of thesedata sources assigned by one user:

-   Antivirus agent: 20%-   Personal firewall agent: 70%-   Anti-spyware agent: 10%

This relative weighting can be represented as a matrix row vector:A=[0.2,0.7,0.1].

The relative weighting matrix and the current state matrix aremultiplied using conventional matrix algebra to yield:

${\begin{bmatrix}0.2 & 0.7 & 0.1\end{bmatrix} \times \begin{bmatrix}80 & 15 & 0 \\60 & 20 & 20 \\0 & 25 & 5\end{bmatrix}} = \begin{bmatrix}16.0 & 19.5 & 14.5\end{bmatrix}$

Therefore, the current assessment of the endpoint's overall complianceusing these sample data sources, sample relative data source weightings,sample data sources attributes, and current state values are:

-   -   Security applications current running using approved vendor        agents compliance score: 16.0    -   Security applications recent updates compliance score: 19.5    -   Security applications current configuration compliance score:        14.5

These compliance scores are compared to policy-defined thresholds inorder to make a compliance assessment. For example assume the followingvalues exist for these policies in the policy store:

-   -   Required current running security agents with approved vendor        compliance score: 20.0    -   Required security agent software and signature files currency        compliance score: 15.0    -   Required security agent current configuration compliance score:        10.0

In this situation, the endpoint is out of compliance with regards tocurrently running security agents and their vendor, in compliance withregards to current configuration settings, and in compliance with regardto configuration settings.

It will now be apparent to the reader that these methods can be extendedand/or modified in a number of ways with regards to the data sources,attributes, data source relative weightings, attribute relativeweightings, compliance thresholds, etc.

Context-Sensitive Threshold and Weighting Adjustments to QuantitativeAnalytical Models for Policy Enforcement

In any of the numeric-base methods supported by the client, examples ofwhich are shown above, scores, thresholds, weightings, etc. may bescaled up or down using a global weighting adjustment or discreteweighting adjustments stored as policy values in the policy data store.Similarly, situation-specific policy-based adjustments can be made toscores and thresholds for other analytical models that may be added tothe policy management system in the future.

For example, a user directly connected to the corporate network likelybenefits from levels of protection or compliance monitoring systemsintegrated by the employer into the local network, reducing thecriticality that one or more security applications are running orcorrectly configured on the user's machine. Therefore, an administratormay wish to relax the minimum compliance score required to be able toaccess the corporate network, or specific computers and/or applicationson the corporate network by a number of points. In this situation, theanalysis engine would query the policy data store for the minimumcompliance score required to allow a certain system event to occur,determine the user's location (e.g. on the corporate network or not), ifon the corporate network determine if the minimum compliance thresholdshould be adjusted by retrieving the policy value for the on-campusnetwork security adjustment policy, adjust the compliance threshold asnecessary, and then finally assess the compliance state of the endpointusing this adjusted threshold.

Other policy-based, situation-specific or context-sensitive adjustmentsare possible based on endpoint state information and such adjustmentcapabilities are supported by the policy management system.

Statistics-Based Analytical Model for Policy Enforcement

Additional analytical methods supported by policy management system 106are based on statistical analysis methods. These methods differ frommethods previously described herein in that compliance analysis methodsdescribed below are based on evaluation of a population sample comprisedof multiple data points collected over a period of time, rather than aevaluation of a single collected data point.

As an illustrative example of single data point methods previouslydescribed herein, the policy management system 106 can be configured viaa policy setting within policy store 106B to query the operating systemor an external agent within endpoint 104 every X seconds, where X is apolicy-defined value (e.g. interval in seconds=60) to determine thevalue of any system metric, e.g. CPU utilization. This value can bepassed immediately to the compliance analysis engine upon collection asan indicator of the instant CPU utilization. In this case, the samplesize is one. The following examples illustrate several of the methodsthe client supports for utilizing larger sample sizes to assesscompliance with regards to CPU utilization.

The ability to apply these methods to other measurable metrics on theendpoint are capabilities of the policy management system. While theexamples cited here utilize CPU utilization as the metric underevaluation, the same capabilities and options can be readily applied toany other numeric endpoint metric including but not limited to:

-   Network bytes received-   Network bytes transmitted-   Physical memory in use-   Queries to virtual memory-   Free virtual memory-   Transaction response time for specific application transactions-   Number of times a specific application transaction occurs-   Number of times an application is opened-   Emails sent-   Emails received-   Email arrival rate (e.g. emails arriving per minute)-   Email reception rate-   Email attachment count-   Email attachment size-   Number of recipients in emails sent-   DNS queries-   DNS queries serviced by local DNS cache-   ICMP messages transmitted-   ICMP messages received-   HTTP requests sent-   HTTP request transmission rate-   File open rate (e.g. files opened per minute)-   Etc.

Additionally, as described previously herein, the analysis engine 106Cis able to apply these methods to ratings or scores that are derivedfrom inspecting numeric or non-numeric attributes of the endpoint,evaluating their state, comparing the current state with policy valuesthat define numeric weightings or scores for a given state of a givenendpoint attribute, and assigning a numeric value to that state. Theassigned numeric value then becomes one data sample of a samplepopulation.

Data Summary-Based Statistical Analysis Methods

The analysis engine 106C is able to utilize statistical analysis methodsfor assessing compliance against a single, related group or arbitrarygroup of numeric conditions for the purposes of calculating a centraltendency value of raw (i.e. reported directly from one of variousexemplary agents 104E) and/or computed (i.e. normalized by passing rawnumeric or non-numeric condition identifiers and values to the analysisengine 106C and having the analysis engine query the policy data store106B to determine the appropriate score to apply to the raw numeric ornon-numeric value) condition(s), comparing the calculated value tocorresponding policy values residing in the policy data store 106B thatdefine compliance value(s) and/or ranges for the data element(s), andmaking an assessment about compliance of that/those data element(s). Thecentral tendency of a value given a sample population is commonly termedan ‘average’, however that is a general term and there are in factseveral statistical analysis methods for calculating the centraltendency of a sample population. The analysis engine 106C does in factsupport several methods as described below. The specific method used forcalculating the central tendency value of a given data element isselected by the operator. It will be apparent to the reader that thenature of the distribution makes certain methods more or lessappropriate or optimal.

Specific averaging methods supported by the analysis engine 106C includethe following.

Mean-Based Analysis Method

Using an average (or mean) statistical analysis method, the complianceanalysis engine is configured to perform a system query (e.g. CPUutilization, antivirus agent compliance, etc.) a policy-defined numberof times, (e.g. count=5) at a policy-defined sampling interval (e.g.interval=60 seconds) and then calculate an average or mean value overthe consecutive data samples. The average or mean value is determined bysumming the values of the collected samples and then dividing the sum bythe number of samples. This calculated average or mean is the valuepassed to the compliance assessment routine at the completion of thesampling window and used in subsequent compliance analyses. An updatedaverage or mean is passed to the compliance assessment routine at afrequency roughly equivalent to the sampling window size, immediatelyfollowing calculation of the mean.

Moving Average-Based Statistical Analysis Method

Using a moving average statistical analysis method, the complianceanalysis engine is configured to perform a system query (e.g. CPUutilization, antivirus agent compliance, etc.) a policy-defined numberof times, (e.g. count=5) at a policy-defined sampling interval (e.g.interval=60 seconds) and then calculate an average or mean value overthe consecutive data samples. The average or mean value is determined bysumming the values of the collected samples and then dividing the sum bythe number of samples. This calculated average or mean is the valuepassed to the compliance assessment process at the completion of thesampling window and used in subsequent compliance analyses. An updatedaverage or mean is passed to the compliance assessment routine at afrequency roughly equivalent to the sampling interval, immediatelyfollowing calculation of the moving average over the last X samples.

Median-Based Statistical Analysis Method

Using a median statistical analysis method, the compliance analysisengine is configured to perform a system query (e.g. CPU utilization,antivirus agent compliance, etc.) a policy-defined number of times,(e.g. count=5) at a policy-defined sampling interval (e.g. interval=60seconds) and then determine the midpoint between the highest and thelowest value among all the collected samples. This median value is thevalue passed to the compliance assessment routine at the completion ofthe sampling window and used in subsequent compliance analyses.

Mode-Based Statistical Analysis Method

Using a mode statistical analysis method, the compliance analysis engineis configured to perform a system query (e.g. CPU utilization, antivirusagent compliance, etc.) a policy-defined number of times, (e.g. count=5)at a policy-defined sampling interval (e.g. interval=60 seconds) andthen determine the value that occurs most frequently among all thecollected samples. This mode value is the value passed to the complianceassessment routine at the completion of the sampling window and used insubsequent compliance analyses. In the event that no mode value exists,which is possible if all values in the sample population are equal, thecompliance analysis engine will pass the average or mean value to thecompliance assessment routine at the completion of the sampling window.

Geometric Mean-Based Statistical Analysis Method

Using a geometric mean statistical analysis method, the complianceanalysis engine is configured to perform a system query, e.g. for TCPsegment window size a policy-defined number of times, (e.g. count=5) ata policy-defined sampling interval (e.g. interval=60 seconds) and thendetermine the geometric mean of the rate of change. For example, when anew TCP connection is opened between the endpoint and a remote serverapplication across the network, the measured values of the segmentwindow size in successive samples might be as follows:

-   Sample 1: 604 bytes:-   Sample 2: 72 bytes (increase of 12.5%)-   Sample 3: 100 bytes (increase of 38.89%)-   Sample 4: 150 bytes (increase of 50%)-   Sample 5: 250 bytes (increase of 606.67%)

In this case the geometric mean is

[1.125×1.3889×1.5×1.6667]^(1/4)−1=0.4058=40.58%

This geometric mean value is the value passed to the complianceassessment routine at the completion of the sampling window and used insubsequent compliance analyses.

Rate-Based Statistical Analysis Method

Using a rate-based statistical analysis method, the compliance analysisengine is configured to perform a system query (e.g. CPU utilization,CPU temperature, number of emails sent, antivirus agent compliancescore, personal firewall compliance score, composite security score,etc.) two times at a policy-defined sampling interval (e.g. interval=100seconds). The analysis engine performs a calculation of the differencebetween the two sampled values (or calculated compliance scores),performs a calculation of the difference between the two sampling times(or alternatively uses the policy-defined sampling interval), anddivides the value difference by the time difference to obtain a rate,e.g. emails per second, change in CPU temperature per second, number ofHTTP requests to a given DNS domain per minute, change in antiviruscompliance score per minute, authentication failures per minute, etc.This rate value is the value passed to the compliance assessment routineat the completion of the sampling window and used in subsequentcompliance analyses. This rate calculation result can also be used bythe client to predict the value of the data element (either raw data orcalculated score) at a future time. This predicted value can be used insubsequent compliance analysis. It will be understood that rates can bedetermined from many other sampling processes.

Acceleration Rate-Based Statistical Analysis Method

Using an acceleration rate-based statistical analysis method, thecompliance analysis engine is configured to perform a system query (e.g.CPU utilization, CPU temperature number of emails sent, antivirus agentcompliance, etc.) two times at a policy-defined sampling interval (e.g.interval=100 seconds). The compliance analysis engine performs acalculation of the difference between the two values, performs acalculation of the difference between the two sampling times (oralternatively uses the policy-defined sampling interval), and dividesthe value difference by the time difference to obtain a rate (e.g.emails per second, change in CPU temperature per second, number of HTTPrequests to a given DNS domain per minute, change in antiviruscompliance score per minute, authentication failures per minute, etc.Rather than passing this value to the compliance assessment routine atthe completion of the sampling window as described in the previousmethod, the compliance analysis engine repeats this activity at a latertime, where the time interval between the first rate sampling window(which collects two samples at a policy-defined sampling interval) andthe second rate sampling window (which collects two additional samplesat the same policy-defined sampling interval) is defined as anacceleration policy setting in the client policy data store.

The compliance analysis engine performs a calculation of the differencebetween the two rate values, performs a calculation of the differencebetween the two sampling times (or alternatively uses the policy-definedacceleration sampling interval), and divides the value difference by thetime difference to obtain a change in rate per unit time (i.e. just asthe physical property acceleration is the measurement of change invelocity per unit time, where velocity itself is the measurement of thechange in distance (the raw value being measured) per unit time. Thisacceleration value is the value passed to the compliance assessmentroutine at the completion of the acceleration sampling window and usedin subsequent compliance analyses. This acceleration calculation resultis also able to be used by the client to predict the value of the rateat a future time. This predicted value can be used in subsequentcompliance analysis.

Variability-Based Statistical Analysis Methods

The compliance analysis engine, is able to utilize statistical analysismethods for assessing compliance against a single, related group orarbitrary group of data elements for the purposes of calculating thevariability value of raw, computed and/or mapped data element(s),comparing the calculated variability value to corresponding policyvalues that define compliance value(s) and/or ranges for the dataelement(s), and making an assessment about compliance of that/those dataelement(s).

Specific variability methods supported by the client are set out below.The specific method that should be used for calculating the variabilityvalue of a given data element or combination of data elements isselected by the administrator, as the nature of the distribution makescertain methods more or less appropriate or optimal for evaluatingcompliance of a given data element or combination of data elements.

Min-Based, Max-Based and Range-Based Statistical Analysis Method

Using a minimum, maximum or range-based statistical analysis method, theclient is configured to perform a system query (e.g. CPU utilization,antivirus agent compliance, etc.) a policy-defined number of times,(e.g. count=5) at a policy-defined sampling interval (e.g. interval=60seconds) and then determine the minimum and maximum values that wereobserved in the collected sample. If range information is necessary, theclient will also calculate the range based on the observed minimum andmaximum. The minimum, maximum and/or values are passed to the complianceassessment routine at the completion of the sampling window and used insubsequent compliance analyses.

Variance-Based Statistical Analysis Method

Using a variance-based statistical analysis method, the client isconfigured to perform a system query (e.g. CPU utilization, antivirusagent compliance, etc.) a policy-defined number of times, (e.g.count=100) at a policy-defined sampling interval (e.g. interval=100seconds) and then determine the variance of the collected sample using astandard formula for calculating sample variances:

$s^{2} = {{\frac{1}{n - 1} \times {\sum\limits_{i = 1}^{n}{\left( {x_{i} - \overset{\_}{x}} \right)^{2}\mspace{14mu} {where}\mspace{14mu} \overset{\_}{x}}}} = {\frac{1}{n} \times {\sum\limits_{i = 1}^{n}x_{i}}}}$

The calculated variance is passed to the compliance assessment routineat the completion of the sampling window and used in subsequentcompliance analyses.

Standard Deviation-Based Statistical Analysis Method

Using a standard deviation-based statistical analysis method, thecompliance analysis engine is configured to perform a system query (e.g.CPU utilization, antivirus agent compliance, etc.) a policy-definednumber of times, (e.g. count=100) at a policy-defined sampling interval(e.g. interval=100 seconds) and then determine the standard deviation sof the collected sample, where the standard deviation is equal to thesquare root of the variance. The method for calculating the variance wasjust described in the preceding variance-based statistical analysismethod description

The calculated standard deviation is passed to the compliance assessmentroutine at the completion of the sampling window and used in subsequentcompliance analyses.

Coefficient of Variation-Based Statistical Analysis Method

Using a coefficient of variation (COV)-based statistical analysismethod, the compliance analysis engine is configured to perform a systemquery (e.g. CPU utilization, antivirus agent compliance, etc.) apolicy-defined number of times, (e.g. count=100) at a policy-definedsampling interval (e.g. interval=100 seconds) and then determine the COVof the collected sample using a standard formula for calculating COV:

${COV} = \frac{{Sample}\mspace{14mu} S\; \tan \; {dard}{\mspace{11mu} \;}{Deviation}}{{Sample}\mspace{14mu} {Mean}}$

Where the sample standard deviation is equal to the square root of thesample variance, and where the sample variance is equal to:

$s^{2} = {{\frac{1}{n - 1} \times {\sum\limits_{i = 1}^{n}{\left( {x_{i} - \overset{\_}{x}} \right)^{2}\mspace{14mu} {where}\mspace{14mu} \overset{\_}{x}}}} = {\frac{1}{n} \times {\sum\limits_{i = 1}^{n}x_{i}}}}$

And where the sample mean is determined by summing the values of thecollected samples and then dividing the sum by the number of samples.

The calculated COV is passed to the compliance assessment routine at thecompletion of the sampling window and used in subsequent complianceanalyses.

Number of Occurrences-Based Statistical Analysis Method

Using a number of occurrences-based statistical analysis method, thecompliance analysis engine is configured to perform a system query (e.g.CPU utilization, antivirus agent compliance, etc.) a policy-definednumber of times, (e.g. count=100) at a policy-defined sampling interval(e.g. interval=1 second) and count the number of occurrences of eachdifferent value collected. The list of values and their frequency ofoccurrence is then passed to the compliance assessment routine at thecompletion of the sampling window and used in subsequent complianceanalyses. This method is useful in situations where the action policy istriggered based on the number of occurrences of a specific value orvalues of a given data element in a sampling window.

Occurrence Frequency-Based Statistical Analysis Method

Using a percentage of occurrence-based statistical analysis method, thecompliance analysis engine is configured to perform a system query (e.g.CPU utilization, antivirus agent compliance, etc.) a policy-definednumber of times, (e.g. count=100) at a policy-defined sampling interval(e.g. interval=1 second) and count the number of occurrences of eachdifferent value collected. The number of occurrences of a given value isdivided by the number of samples to determine the relative frequency ofoccurrence of that value. This will normally be expressed as a decimalvalue or a percentage. The list of values and their frequency ofoccurrence is then passed to the compliance assessment routine at thecompletion of the sampling window and used in subsequent complianceanalyses. This method is useful in situations where the action policy istriggered based on the relative frequency of occurrences of a specificvalue or values of a given data element in a sampling window.

Cumulative Distribution-Based Statistical Analysis Method

Using a cumulative distribution-based statistical analysis method, thecompliance analysis engine is configured to perform a system query (e.g.CPU utilization, antivirus agent compliance, etc.) a policy-definednumber of times, (e.g. count=100) at a policy-defined sampling interval(e.g. interval=1 second), sort the collected samples in ascending order,count the number of occurrences of each different value collected anddetermines the relative frequency of each value as described above. Thecompliance analysis engine then calculates the cumulative frequencydistribution of each value by adding the relative frequency of thatvalue to the sum of the relative frequencies of all lesser values. Thelist of values and their cumulative frequency of occurrence is thenpassed to the compliance assessment routine at the completion of thesampling window and used in subsequent compliance analyses. This methodis useful in situations where the action policy is triggered when therelative cumulative frequency exceeds a policy-defined threshold. Forexample, analysis of a sample of 100 transactions of type X concludesfor this population sample that 90% of the transactions completed within3.5 seconds. This result is compared to a predefined policy in thepolicy data store that specifies that 90% of type X transactions mustcomplete within 4 seconds to determine whether or not a condition existsthat warrants taking a policy-defined action on the endpoint.

Sampling Distribution-Based Statistical Analysis Method

Using a sampling distribution-based method, an administrator may measuresuccessive values of a data element of interest a large number of timesin either a controlled or typical endpoint environment to determine thedistribution type, mean, variance and standard deviation of the valuesof that data element. Alternatively an administrator may define a targetmean and standard deviation he believes reasonably describes thedistribution of the values of the data element of interest. These valuesare stored in the client policy data store as policy values such thatthey can be changed in the future as needed.

Alternatively a policy can be enabled in the compliance analysis enginethat causes the compliance analysis engine to monitor a particular dataelement for a period of time until a sufficiently large sample toaccurately represent the population of possible data values iscollected, and then calculate a mean and standard deviation for the verylarge sample. These values also can be stored in the client policy datastore as target policy values that represent the steady state behaviorof that particular data element. The monitoring and data collectionactivity performed by the client can be started or stopped at any timeusing policy settings or commands issued to the client. The calculatedproperties (e.g. mean, standard deviation, etc) can be discarded at apolicy-defined interval (e.g. every 60 days) or date (e.g. Dec. 31,2005) and the procedure repeated, such that the client periodicallyrefreshes the values stored in the policy data store that describe thepopulation.

These values can further be used to calculate the probability of asample event having a value greater than a specified policy value, lessthan a specified policy value, or within a specified range of policyvalues. This capability is supported in the client by transforming thesample value into a normal random variable with mean equal to zero and avariance of one. This transformation is done by subtracting thepopulation mean specified value and dividing the result by thepopulation standard deviation. The client includes a standard normaldistribution data table in its local data store for looking up theprobability of a given value or range of values of this transformed ornormalized random variable.

The compliance analysis engine also allows an administrator to specify amean and/or variance threshold relative to the population's mean and/orvariance for a given value of a given data element or group of dataelements. The compliance analysis engine can be configured to perform asystem query (e.g. CPU utilization, antivirus agent compliance, etc.) apolicy-defined number of times, (e.g. count=100) at a policy-definedsampling interval (e.g. interval=1 second). The mean, variance and/orstandard deviation of the sample can be calculated using standardmethods such as those previously described. The calculated properties ofthe sample (e.g. mean, standard deviation) are then passed to thecompliance assessment routine at the completion of the sampling windowand compared by the compliance analysis engine to the policy-definedvalues that describe the population and that were previously defined bythe administrator or calculated by the client. This method is useful insituations where the action policy is triggered when the properties of asample, e.g. the mean or standard deviation, exceeds a policy-definedthreshold. For example, the client locally observes a population sampleof 100,000 events of a particular type, calculates the mean and thestandard

Linear Regression-Based Analysis Method

Using a linear regression-based method, an administrator may measuresuccessive values of an (x, y) data pair of interest comprised of anindependent and a dependent variable. The measurement may occur a largenumber of times in either a controlled or typical endpoint environmentto determine the coefficients (a, b) of a line equation that representsthe relationship between the dependent variable (y) and the independentvariable (x) using the standard line equation y=ax+b. Alternatively anadministrator may define target coefficients he believes reasonablydescribes the fitted relationship of the values of the data pair ofinterest. These values are stored in the client policy data store aspolicy values such that they can be changed in the future as needed.

When the required number of samples is collected, the administratorutilizes the method of least squares for estimating the regressioncoefficients (a, b) of a line equation that represents the relationshipbetween the dependent variable (y) and the independent variable (x)using the standard line equation y=ax+b, where

$b = \frac{{n{\sum\limits_{i = 1}^{n}{x_{i}y_{i}}}} - \left\lbrack {\left( {\sum\limits_{i = 1}^{n}x_{i}} \right) \times \left( {\sum\limits_{i = 1}^{n}y_{i}} \right)} \right\rbrack}{{n{\sum\limits_{i = 1}^{n}x_{i}^{2}}} - \left( {\sum\limits_{i = 1}^{n}x_{i}} \right)^{2}}$

and where

$a = \frac{{\sum\limits_{i = 1}^{n}y_{i}} - {b{\sum\limits_{i = 1}^{n}x_{i}}}}{n}$

These values are then stored as policy values.

There are several analyses methods supported by the client that cansubsequently make use of these policy values. In one method supported bythe compliance analysis engine, the compliance analysis engine isconfigured to perform a system query of a data pair of interest (e.g.response time as a function of number of bytes or records in transactionrequest, number of network messages transmitted per minute as a functionof number of active programs, etc.) a policy-defined number of times,(e.g. count=50) at a policy-defined sampling interval (e.g. interval=1second) or when the event actually occurs, (e.g. a message being sent toa specific remote computer). A mathematical analysis is performed tocalculate the actual regression coefficients of the sample. Thecalculated coefficients of the sample are then passed to the complianceassessment routine and compared by the client to the policy-definedvalues. A compliance assessment is subsequently made.

In another method supported by the compliance analysis engine, thepolicy-defined coefficients are combined with the sampled value of theindependent variable (x) to determine an estimated value of thedependent variable (y). The actual value of the dependent variable (y)is then compared to the estimated value of the dependent variable (y).If the actual value differs from the estimated value by more than aspecified, policy-defined difference (positive, negative and/or absolutemagnitude), a policy violation is deemed to have occurred.

In another method supported by the compliance analysis engine, theactual regression coefficients of the sample are used to predict thevalue of the dependent variable given a value of the independentvariable. The predicted value of the dependent variable can then be usedas a dynamically derived policy value. Should the specified value of theindependent variable occur in the future, the actual value of thedependent variable at that time is compared by the compliance analysisengine with the dynamically derived policy value. If the actual valuediffers from the predicted value by more than a specified,policy-defined difference (positive, negative and/or absolutemagnitude), a policy violation is deemed to have occurred.

Filtering Analysis

Another analytical method supported by the client is based on filteringtheory. A filter in this context is a piece of purpose-built softwarethat analyzes a particular data set, applies a threshold function ofsome type to that data set, and extracts only information of interest.Filtering in this context therefore is the act of extracting interestingdata by applying a threshold against individual data points within adata set. Examples of the types of data the client can collect andpolicy-based thresholds the client can evaluate were previouslydescribed above.

The compliance analysis engine supports several different filteringapproaches and is extensible to support future additional filteringapproaches as well. One supported filtering method previously describedinvolves by collecting a specific type of data from the environment,comparing the data point against policy-defined thresholds, and taking apolicy-based action when a compliance threshold is exceeded.

In another filtering method supported by the client, the complianceanalysis module assumes a particular aspect of the endpoint is incompliance unless otherwise notified by the data collection module. Inthis filtering approach, the filtering method continuously collects aspecific type of data from the environment and performs a comparison ofthat single point of data against the policy-defined threshold for thatsingle point of data. Only when a compliance violation is detected, isthe data, or alternatively a descriptive message identifying thecompliance violation, passed to an alternate compliance analysis engineresponsible for combining the results of assessments of individual datapoints, i.e. performing a holistic compliance assessment. The overallcompliance analysis module assumes complete compliance with respect toany given data element unless it is informed otherwise. This is commonlyreferred to as an exception-based notification system. It is anadvantageous approach as the software routine responsible fordetermining overall assessment has to process less data and thus canmore quickly reach decisions with respect to required policy enforcementactions.

In a statistical approach to smoothing and prediction, there must becertain statistical parameters available such as a mean function or acorrelation function. In particular, there must be a difference betweenthe values of these functions for the interesting information and thefunction values for the noise. The filter is set to pass interestinginformation and filter noise by setting the filtering levelappropriately.

Application of Methods to All Endpoint State Data Elements

Many of the examples of analysis methods described herein for measuringquantitative endpoint state information utilize one or two endpoint dataelements (e.g. CPU utilization, antivirus agent compliance score) as anexample. These same data elements are cited as examples throughoutsimply for reader convenience, and the reader will realize that theinvention is not thus limited. The policy management system fullysupports the ability to apply these methods to any number of endpointdata elements, either raw or derived as a result of an upstreamcompliance assessment and calculation performed by the policy managementsystem.

Application of Methods to Non-Numeric Endpoint State Information

The above-described models and methods for measuring quantitativeendpoint state information can be applied to non-numeric complianceassessments by mapping the environmental data to numeric values usingpolicy-defined values, as noted above.

As an example, the state of the antivirus agent and a review of policysettings might result in an antivirus compliance score of 65 points or65%. Rather than treat this as a single data point and form an immediatecompliance assessment, it might be preferable to sample the antivirusagent state information at a periodic interval for a period of time,where both the sampling interval and sampling window are policy-definedvalues, calculate the compliance score at each sampling, and treat thecollection of compliance scores as a population sample. Suchcapabilities are supported by the policy management system. While thisexample cites the translation of antivirus agent state information intoan antivirus compliance score, translation of other endpoint stateinformation such as those data elements previously identified hereininto compliance scores is also supported by the present invention.Collection of population samples of numeric compliance scores for otherpieces of endpoint state information is likewise supported by thepresent invention.

Application of Analytical Methods to Composite Endpoint ComplianceAssessments

Just as statistical and other analytical methods can be applied tocompliance assessments of discrete data elements (e.g. CPU utilization)or data sources (e.g. antivirus agent state including version, runningstate, vendor, date of last signatures update, configuration settings,etc.), these methods can also be applied to composite complianceassessments.

In a previous example, the real time compliance assessment at a givenpoint was as follows:

Agent Adjusted Raw score Threshold Relative score Sensor (points)(points) Weighting (points) Antivirus agent 65 75 15% 9.75 Personalfirewall agent 93 90 70% 65.1 Antispyware agent 0 70 100% 0 Contentfiltering agent 100 60 5% 5 Composite Score 79.95 Composite Threshold75.00

As previously mentioned, the policy management system is able to usestatistical and other analysis methods to calculate one or more rawscore inputs into this composite score.

The policy management system is also able to use statistical analysismethods cited above, including but not limited to mean, median, mode,moving average and geometric mean to calculate a composite score byapplying a statistical analysis method to a population sample ofindividual composite scores calculated at different times. Samplingintervals and sample count are controlled via policy settings. Thepolicy management system is able to perform this function using all ofthe statistical analysis methods previously described. The client isable to perform this function for all monitored data elements and allcomposite scoring functions.

Exception Reporting of Analyses Result

Recalling the exception-based optional approach previously described, inanother embodiment the instant CPU utilization, the average CPUutilization, or moving average CPU utilization can be reported everytime the value is determined, or only reported when it exceeds a policydefined threshold.

Non-Exclusivity of Analyses Methods

In the examples cited, the instant CPU utilization, average CPUutilization, moving average, etc. are distinctly different dataelements, however the different data elements can be used simultaneouslyfor different compliance evaluation purposes, i.e. collection and usageof instant CPU utilization and average CPU utilization are not mutuallyexclusive. For example, one compliance evaluation method may require theinstant CPU utilization value in order to perform a complianceevaluation, whereas a different compliance evaluation method maysimultaneously require the average CPU utilization in order to perform acompliance evaluation. The present invention supports the ability to usethese different measurement methods for different compliance tests usingthe same data source simultaneously. The present invention furthersupports this simultaneous use capability for all other supportedmonitored data sources as well, including both numeric sources andnon-numeric sources that are converted to numeric values or scores.

Combining Analyses Methods

In the examples cited herein, average, mode, moving average, coefficientof variation, standard deviation, etc. are different analysis methodssupported by the policy management system. It will be understood thatthe policy management system provides the ability to use logicalcombinations (e.g. AND, OR, ELSE, IF, THEN, NOT, etc.) of differentcompliance measurement methods for performing compliance evaluation ofthe same data element or group of data elements simultaneously. Examplesof policy-driven capabilities of the policy management system include:

-   -   CPU utilization policy: Median of past 100 consecutive samples        must be less than 98% AND trailing 5 minute moving average must        be less than 80%    -   File open rate policy: Mean of past 5 consecutive samples must        be less than 100 AND standard deviation on those same samples        must be less than 7.    -   Antivirus compliance policy: Most recent calculation of        antivirus compliance based on most recent antivirus state        inspection must have a compliance score greater than 50 OR mean        of past 5 consecutive samples must be greater than 70.

The policy management system supports this simultaneous use capabilityfor all other supported monitored data elements as well, including bothnumeric sources and non-numeric sources that are mapped to numericvalues or scores.

Similarly, it is important to note that simultaneously used combinationsof these methods, as well as other methods cited herein are possible andare supported by the policy management system. For example, the businessrules method cited previously could be used for compliance monitoringand enforcement with regards to physical ports on the endpoint, such asUSB ports, serial ports, printer ports, IR or RF communication ports,etc., while the Boolean rules method cited previously could be used forcompliance monitoring and enforcement with regards to permittedapplications, while a matrix algebra method could simultaneously be usedfor compliance monitoring and enforcement with regards to networkconnectivity or VPN tunnel establishment. Other combinations are ofcourse possible as well. These combinations are considered in accordancewith one of the above-described methods, for example in Booleancombinations or as otherwise described herein. Such combinations arealso supported by the policy management system.

Real Time Adjustment of Sampling Frequency

When an endpoint is compliant with security policies, a reduction inendpoint inspection frequency reduces the load on the system, e.g.memory, file access, etc. Conversely, when an endpoint is out ofcompliance, and in particular when certain critical security situationsexist, it is appropriate to inspect the endpoint with much higherfrequency so that a highly up-to-date view of the endpoint's stateexists at all times. Therefore condition data relating to monitoreditems (e.g. CPU utilization, antivirus compliance score, security agentscomposite compliance score, etc.) can be collected at different samplingintervals, for thresholds (or the range) above which or below which thenew sampling frequency parameters take effect. The policy managementsystem provides the ability to support this very capability through theuse of policy settings where these parameters can be specified andconfigured.

Managing Endpoint and Host Operation

Continuing with reference to FIG. 2, when policy violations are detectedit may be desired to take one or more discrete actions to either bringthe endpoint into compliance, prevent harm from coming to the localand/or remote computers, restrict user actions, or perform any number ofdifferent actions (step 212). Examples of discrete actions which may beinitiated by policy management system 106, and executed by host system102, and endpoint system 104, include those set out below. The solutionis extensible to allow additional actions to be added in the future andconfigurable to allow different groups to customize different actions tobest meet their needs. It will be understood that that the process ofmanaging the endpoint and host operations repeats as frequently asnecessary (step 214). As noted herein above, it may be desirable torepeat the steps, including the collection of data, analysis of data,and the management of the systems, multiple times during a singleconnection session.

With reference back again to FIG. 4, there is shown how the abovedescribed operation of compliance analysis engine 106C results in thegeneration of output actions 402, these actions used to control theoperation of the agents within the endpoint. These policy actions areselected based upon the above-described comparison of the state of theconditions 14F in comparison to the compliance rules in data store 16B,and specify actions to permit, prevent or automatically initiate on theendpoint. Policy actions may be endpoint actions allowed to take placebecause the endpoint system 104 is in compliance with security policies,actions to take to partially or wholly restrict access to endpointresources because the endpoint system 104 is not in compliance withsecurity policies, or a combination thereof. Additionally, the inventionmay log event information locally in the policy data store and/or createand transmit event and state information across a data communicationsnetwork to a remote policy management system 106 or a remote computerfor logging, operator notification, transaction triggering, reporting,or other administrative purposes. FIG. 4 in particular illustrates thenotion of endpoint agent closed loop control feedback as a central partof the invention where endpoint policy actions taken may be targeted toa one or more specific endpoint agents 104E as a direct result ofendpoint condition information 104F obtained from that endpoint agent104E and other various exemplary agents. For example the antivirus agentmay be queried for its current state. That information may then becombined with other information from other endpoint agents and analyzedby the analysis engine 106C to determine if any noncompliance conditionsexist. If so, the invention may direct the antivirus agent to takespecific actions, change internal configuration settings, etc. to bringthe endpoint back into compliance or to block or permit certain systemor operator activities.

Examples of specific endpoint policy actions the invention is able totake are itemized previously in this document. Additional examples ofendpoint actions the invention is able to take are now shown:

-   Certificate Actions    -   Grant or deny access to a locally stored digital certificate    -   Transmit a certificate revocation request message-   Login Account Actions    -   Disable login account    -   Expire password    -   Initiate password reset    -   Automatically log a user out of an application, the system, a        secure connection, etc.-   Operating System Actions    -   Halt a named memory-resident process    -   Delete a specific file    -   Rename a specific file    -   Change the attribute of a file from read/write to read only, or        reverse    -   Change the attribute of a folder from read/write to read only,        or reverse    -   Etc.-   System Hardware Actions    -   Enable or disable a parallel port    -   Enable or disable a serial port    -   Enable or disable a USB port    -   etc-   Application Actions    -   General:        -   Launch a named application        -   Uninstall a named application        -   etc.    -   Email:        -   Adjust bandwidth available to email application        -   Remove recipients from outbound emails        -   Discard email        -   etc.    -   Application transactions        -   Initiation or blocking of specific transaction types for            named applications-   VPN Client Actions    -   Establish VPN tunnel    -   Disconnect VPN tunnel    -   Establish VPN tunnel to a specified VPN server    -   Update VPN profile-   Antivirus Agent Actions    -   Delete a malicious file    -   Quarantine or otherwise disable a malicious file    -   Quarantine or otherwise disable infected files-   Personal Firewall Agent Actions    -   Block outbound access from specific application(s)    -   Block outbound access to specific destination IP address(es)    -   Block outbound from specific communication protocols, e.g. TCP,        HTTP, policy management client-server protocol, etc.)    -   etc.-   Content Filtering Agent Actions    -   Block outbound access to specific DNS hostnames (e.g.        www.cnn.com,) or specific realm(s) (e.g. *.si.com)-   Spyware Management Agent Actions    -   Delete a malicious file    -   Quarantine or otherwise disable a malicious file    -   Prevent specific software from loading into memory-   File System Actions    -   Delete a named file    -   Set the attributes of a named file to read-only    -   Move a named file to a specified local or remote location    -   etc.-   Data Backup Actions    -   Initiate partial or full backup    -   Initiate a local or remote backup of selected files and/or        folders.    -   Suspend a backup process    -   Restart/Resume a backup process-   Data Access Actions    -   Restrict access privileges to specific data sources    -   Block write access privileges to specific data sources    -   Restrict copy privileges for specific data sources    -   etc.-   Network Connectivity Actions    -   Permit or deny network connectivity on a named dial adapter    -   Permit or deny network connectivity on a named network adapter    -   Change TCP window size to throttle bandwidth consumption for all        applications, for selected applications, for all communication        protocols, and/or for selected communications protocols, for        traffic destined to a specific destination IP address or address        range, etc.    -   etc.-   Network Services Actions    -   Disable DNS    -   Add default DNS server to endpoint configuration settings    -   Add entry to hosts file    -   etc.-   Access Control List Actions    -   Permit or deny network access to an enumerated list of IP        addresses or IP network numbers    -   Permit or deny network access to an enumerated list of TCP or        UDP ports or port ranges    -   Permit or deny network access to an enumerated list of        applications-   Alerting Actions    -   Sending an email alert to a named email address    -   Send an alert to the user interface so the user is aware of the        endpoint's state    -   Send an alert to the user interface so the user is aware of the        policy violations    -   etc.-   Logging Actions    -   Log the policy violation(s) detected on the local machine    -   Send a policy violation(s) log message to a remote machine-   User Actions, Applications, Results, Restrictions, etc. Dimension    (the OUTPUT dimension)    -   Allow certain applications    -   Block VPN connectiivty    -   Execute remediate actions (could be nested depending upon issues        remediated)    -   etc.

As noted and described above, examples and illustrations throughout areillustrative and not limiting. Numerous others will occur to the reader.

Having analyzed conditions and compared existing conditions to requiredconditions as described in the policy data store 106B, the analysisengine 106C determines what actions to initiate (step 212). The analysisengine 106C and it's operative models and algorithms provide the abilityto proactively take an exhaustive and extensible list of permissive,corrective or restrictive actions. The actions can be taken immediately,scheduled to occur at some future point in time, upon completion of somepredefined system event, or as a prerequisite to some predefined systemevent. The actions when taken can also be logged by the agent and madeavailable to a central management reporting console. Also, the actionsmay result in notifications or alerts being displayed to the end user,and/or uploaded to a central management reporting console.

For example the analysis engine can initiate the following actions:

-   User management:    -   Disable a login account    -   Automatically log a user out of an application, the system, a        secure connection, etc.    -   Require an immediate password reset.-   Application management:    -   Launch a specified application    -   Tear down a specified application    -   Prevent a specified application from launching    -   etc.-   Operating system management    -   Reprioritize running processes and threads-   Network management:    -   Prevent network connectivity to a local network    -   Prevent network connectivity to a remote network    -   Prevent use of one or more network adapters    -   Etc.-   Personal firewall:    -   Modify access control policies being enforced by firewall    -   Update access control list-   VPN client:    -   Change tunnel state    -   Force profile-   Data management:    -   Restrict access privileges to specific data sources    -   Block write access privileges to specific data sources    -   Restrict copy privileges for specific data sources    -   etc.-   Application specific management:    -   Initiation or blocking of specific application transaction types-   IT notification:    -   upon a new, previously unknown event, send an alert to a mail        server to notify IT admin describing the issue and providing 2        links, one for approve, one for deny. IT selects one, clicks        link, and is taken to web page where he logs in and submits the        policy definition.

With respect to actions that may be initiated regarding hardwareremediation:

-   Issue end point software and/or hardware information update to IT    asset management system-   Issue repair request to IT computing hardware repair system-   Issue device theft/loss message to IT asset management tracking    system-   etc.    Communication of Endpoint State Information, Endpoint Compliance    Analysis Results and/or Compliance Actions to a Remote Computer

There are a number of alternative implementations for how the inventioncan be instantiated and operated. Several examples of implementationmethods supported by the invention follow. This list is exemplary andnot exhaustive; others will now be apparent to the reader.

Implementation Method 1—Endpoint System 104 Only

In this implementation scenario, all components are deployed on theendpoint system 104 being managed. This implementation is representativeof a consumer-type offering where the system owner, invention operator,system administrator and invention administrator roles are all performedby the same single person. In this implementation, the logicalcomponents of the invention might be distributed across differentsystems as follows:

-   Endpoint system 104 components:    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis engine    -   Policy-based actions    -   Policy data store    -   Policy management functions    -   Reporting functions    -   (all as described above)-   Policy management system 106 components:    -   None-   Host system 102 components:    -   None

Implementation Method 2—Centralized Endpoint System Policy Management

In this implementation, a central management user interface 106A on thepolicy management system 106 is used to configure policies that are thensaved to a central policy data store 106B. The policies are synchronizedor replicated to local policy databases residing in the endpoint system104, for example in data store 104B, on a periodic basis when theendpoint system 104 checks in with the policy management system 106 tosee if updates are available. An analysis engine, performing generallythe same functions as engine 106C, residing on the endpoint system 104is responsible for enforcing all compliance policies on the endpointsystem 104 in accordance with policies received from the policymanagement system 106. This implementation is representative of acorporate-type offering or a managed services-type offering as might beprovided by a service provider firm, where the endpoint system user isdifferent from the endpoint system administrator or inventionadministrator roles. In this implementation, an exemplary distributionof invention components across different systems is as follows:

-   Endpoint system 104 components:    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis engine    -   Policy-based actions    -   Policy data store    -   Policy management console    -   Reporting console    -   (all as described above)-   Policy management system 106 components:    -   Policy data store    -   Policy management functions    -   Reporting functions-   Host system 102 components:    -   None

In this implementation, conditions information, compliance violationsand policy enforcement actions can be logged locally on the endpointsystem 104 and/or uploaded to any remote computer over a datacommunications network for centralized management reporting purposes.Data received from multiple endpoint systems 104 can also be aggregatedfor additional management reports. Information logged locally on theendpoint system 104 can also be viewed locally on the endpoint system byan operator of that system.

Implementation Method 3—Centralized Host System Policy Management

In this implementation scenario, a central management user interface106A on the policy management system 106 is used to configure policiesthat are then saved to a central policy data store 106B. The policiesare synchronized or replicated to a local policy database residing onthe host system 102, for example in data store 102B, on a periodic basiswhen the host system 102 checks in with the policy management system 106to see if updates are available. An analysis engine residing on the hostsystem 102, performing generally the same functions as described withrespect to engine 106B, is responsible for enforcing all compliancepolicies on the host system 102 in accordance with policies receivedfrom the policy management system 106. This implementation isrepresentative of a client-server type application environment whereclient applications (e.g. web browser, database client, etc.) residingon endpoint systems 104 initiate communication sessions with serverapplications (e.g. web server, database management system, etc.)residing on host system 102 to upload and/or downloadapplication-specific data. In this type of client-server environment, itis important to ensure the host system 102 is protected at all times sothat the host system 102 can not be compromised by a rogue endpointsystem 104, or so that the host system 102 is prevented from sendingmalicious data or software code to endpoint system 104. In thisimplementation, an exemplary distribution of invention components acrossdifferent systems is as follows:

-   Endpoint system 104 components:    -   None-   Policy management system 106 components:    -   Policy data store    -   Policy management functions    -   Reporting functions    -   Host system 102 components:    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis engine    -   Policy-based actions    -   Policy data store    -   Policy management functions    -   Reporting functions    -   (all as described above)

In this implementation, conditions information, compliance violationsand policy enforcement actions can be logged locally on the host system102 and/or uploaded to any remote computer over a data communicationsnetwork for centralized management reporting purposes. Data receivedfrom multiple host systems 102 can also be aggregated for additionalmanagement reports. Information logged locally on the host system 102can also be viewed locally on the endpoint system by an operator of thatsystem.

Implementation Method 4—Centralized Analysis Engine and ComplianceAnalysis of Individual Systems

In this implementation scenario, a policy management system 106 is usedto configure compliance policies that are then saved to a policy datastore 106B. Policies are also defined that identify what conditions 104Fshould be monitored by the agent monitoring components 104D, E residingon endpoint system 104 and/or host system 102. These policies are alsostored in the policy data store 106B. Monitoring policies aresubsequently distributed to endpoint system 104 and/or host system 102periodically. An agent monitoring module residing on the endpoint system104, performing generally this same functions as described with respectto engine 106B, collects endpoint condition information 104F andtransmits it to the policy management system 106 where complianceanalysis is performed using an analysis engine 106C. The analysis engineresiding on the endpoint system (or equally the analysis engine residingon the host system 102) does not perform compliance analysis. Theanalysis engine 106B in the policy management system 106 decides whatpolicy enforcement actions are necessary. The policy enforcementdecisions are sent from the policy management system 106 to the endpointsystem 104 or the host system 104 as appropriate where the local systemexecutes the policy enforcement actions as instructed by the policymanagement system 106. In this implementation, an exemplary distributionof invention components across different systems is as follows:

-   Endpoint system 104 components:    -   Endpoint state data collection of conditions    -   Policy-based actions-   Policy management system 106 components:    -   Policy data store    -   Policy management functions    -   Reporting functions    -   Compliance analysis engine    -   Identification of policy-based actions to take-   Host system 102 components:    -   Endpoint state data collection of conditions    -   Policy-based actions

Implementation Method 5—Centralized Analysis Engine and ComplianceAnalysis of Multiple Systems

In this implementation scenario, a policy management system 106 is usedto configure compliance policies that are then saved to a policy datastore 106B. The policy management system 106 can create one set ofcompliance policies it uses locally in its own analysis engine 106B andone or more sets of compliance policies it distributes to endpointsystems. Different sets of compliance policies may have the same ordifferent values regarding items monitored, compliance thresholds,analysis methods to use, etc. Policies are also defined that identifywhat conditions 104F should be monitored by the agent monitoringcomponents 104C, D residing on endpoint system 104 and/or host system102. These policies are also stored in the policy data store 106B.Monitoring policies are subsequently distributed to endpoint system 104and/or host system 102 periodically. An agent monitoring module residingon the endpoint system 104, performing generally the same functions asdescribed with respect to engine 106C, collects endpoint conditioninformation 104F and forwards the aggregate data set of endpointcondition information 104 to the local analysis engine residing on theendpoint system.

A host system 102 if similarly configured would behave in a similar way.Thus the analysis engine local to the endpoint system collects endpointstate data, performs local compliance analysis and makes local policyaction decisions. In addition to the local system (endpoint system 104and/or host system 102) analyzing the condition information, the localsystem uploads the information to the policy management system 106. Theanalysis engine 106C residing in the policy management system 106examines the aggregated set of condition information across multiple orall endpoint systems simultaneously using one or more analytical methodspreviously described herein, e.g. a statistical analysis method, inorder to look for trends across the endpoint population and to assessthe overall level of compliance across the entire endpoint systempopulation or across a specific endpoint system population sample, andwill reach compliance decisions that are independent of and indeed maybe different from compliance decisions made on endpoint systems due todifferent policy values used by the endpoint analysis engine and thepolicy management system analysis engine. The policy management system106 will subsequently identify one or more policy enforcement actionsthat need to be taken, identify specific endpoint systems 104, 102 onwhich those actions need to be taken and send messages to theappropriate endpoint systems containing policy enforcement instructions.The policy management system will also send one or more policyenforcement action instructions to network access control devices suchas VPN gateway, router, switch, remote access server, etc.

In this implementation, an exemplary distribution of inventioncomponents across different systems is as follows:

-   Endpoint system 104 components:    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis    -   Policy-based actions    -   Policy data store    -   Policy management functions    -   Reporting functions-   Policy management system 106 components:    -   Policy data store    -   Policy management functions    -   Reporting functions    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis engine    -   Identification of policy-based actions to take and        identification of specific endpoint and/or host systems that        should take those actions.-   Host system 102 components:    -   Endpoint state data collection of conditions    -   Endpoint state data analysis    -   Compliance analysis    -   Policy-based actions    -   Policy data store    -   Policy management functions    -   Reporting functions

Implementation Method 6—Policy Management System as In-Band AccessControl Mechanism

In this implementation scenario, a policy management system 106 is usedto configure compliance policies that are then saved to a policy datastore 106B. Policies identify what conditions 104F should be monitoredby the agent monitoring components 104C, D residing on endpoint system104 and/or host system 102. The policy management system is integratedwith a network access control function such that user or applicationdata exchanged between endpoint system 104 and host system 102 must passthrough the combined policy management system/network access controlfunction. When the endpoint system 104 tries to access the host system102, the access control function challenges the endpoint system 104 toprovide condition information (i.e. inputs to the endpoint analysisengine) and/or compliance evaluation results (i.e. outputs from theendpoint analysis engine). When the endpoint system 104 returns therequested information, the access control function relays theinformation to the policy management system 106.

The policy management system 106 evaluates the compliance state of theendpoint system 104 based on information provided by the endpoint system104 and policy data residing in the policy management system policy datastore 106B. The policy management system 106 then makes one or moreaccess control decisions. Access decisions might result result inunrestricted access, total denial of access or partially restrictedaccess (e.g. specific destination IP addresses, address ranges,applications, protocols, etc.) to network resources such as applicationsresiding on host system 102. The access control decisions made by thepolicy management system 106 are passed to the access control function.The access control function then automatically configures one or moreaccess control rules for that endpoint system 104. Thereafter allendpoint system 104 data traffic sent through the access controlfunction is either permitted or blocked in accordance with those accesscontrol rules. The access control function periodically issueschallenges to the endpoint system 104 over the life of a communicationssession. The challenge requires the endpoint system 104 to re-submitcompliance information in order to be permitted to maintain an activesession with the network access function.

As the policy management system functionality and the access controlfunction are two separate functions, they can be installed together on ashared computing device or alternatively can be installed separately ontwo different computing devices interconnected by a data communicationsnetwork.

It will be obvious that the several methods just described arecomplementary. As such, various combinations of these methods arepossible and are within the scope of this invention.

Data Sharing

The raw condition information collected by the agent monitor 104C, thecompliance analysis conclusions reached by the analysis engine 106C,and/or compliance actions identified as necessary by the analysis engine106C is available to external security-centric or other software agentsrunning on the same system via the invention's API. The information isalso available to remote systems via data communications networks andtraditional client-server communication protocols (e.g. HTTP) orpeer-to-peer communications protocols. This allows information collectedor conclusions created by the invention to be utilized by other softwareand network access agents as part of their host or network assessmentprocess.

While the various endpoint, host and policy management systems aredescribed as communicating directly with one—another, it will beunderstood that the invention is not thus limited. Numerous intermediaryparties may be associated with the collection and forwarding of agentinformation from endpoint system 104 to policy management system 106.Further, numerous additional intermediary systems may be associated withcommunicating the policy assessment and action information from policymanagement system 106 to host system 102.

Remote Administrator Notification and Control

The analysis engine 106C can be configured via policy settings to send amessage to an administrator via a conventional data communicationsnetwork and a commonly available data communications protocol, (e.g. viaPOP, SMTP, FTP, HTTP, etc.) when a specific policy event occurs, forexample a specific noncompliance condition. Additionally, messages canbe sent to an administrator when an unrecognized event occurs. In oneembodiment, a message could be sent from the client to a policy-definedserver using email or any other communication method. The server wouldin term create or forward an email message to a policy defined emailaddress. The email can contain a description of the event and 2 links:One to approve the action and one to deny the action. Clicking on thelink would causes the IT administrator's web browser to open and send anHTTP request to the policy management server. There the IT administratorcan be authenticated and prompted to confirm his intent and desires onthe particular policy question. When the IT administrator successfullyauthenticates and submits the transaction, the policy setting is updatedon the server policy data store. The next time the client (or any clientin the same policy group) checks in with the server, it willautomatically retrieve and apply the updated policy. Other embodimentsfor sending policy event notifications to an administrator are alsopossible and are within the scope of this invention.

There have thus been provided new and improved methods and systems forsecuring access to electronic resources, for example remote access to ahost system and resources. The present invention applies one or morecompliance assessment algorithms to collected system conditions,comparing the results to a security policy to determine if the system isin compliance with a security policy. One or more actions may be takenresponsively. The present invention can use one or more of a variety ofalgorithms to assess large numbers of state conditions, making decisionsbased upon an essentially infinitely flexible security policy. Theinvention has commercial application in the field of electronic resourcesecurity.

Advantages of the invention include, without limitation:

-   -   Heightened awareness and dynamic, autonomous adjustment to alert        and enforcement thresholds based on condition data.    -   Having a host system self-modulate what local resources are        allowed to be accessed by remote systems based on its own        self-assessment of conditions    -   Having a policy management system alert a host system regarding        conditions on the network as a whole (i.e. a plurality of end        points) or specific end points and either A) explicitly instruct        the host system regarding what local resources can be accessed        by remote systems, or B) alert the host of conditions such that        the host is able to incorporate this data into its own self        assessment and subsequently self-modulate what local resources        are allowed to be accessed by remote systems based on its own        self-assessment of conditions    -   The use of industry standard vulnerability scores or risk        indexes associated with published vulnerabilities, i.e. para        0053 and subsequent    -   The conversion of non-quantitative end point state info, or        conditions into quantitative values    -   The use of quantitative analysis models in end point inspection,        analysis and policy enforcement    -   Extensibility to support different and future quant models    -   Simultaneous and concurrent use of different analysis models,        both quant and non quant to inspect different aspects of the end        point    -   Granular inspections and a wide multitude of conditions data        collected) in order to assess the end point state from a more        holistic level    -   Granular and wide ranging policy enforcement capabilities, i.e.        ability to influence a number of agents and conditions        simultaneously    -   Specific use of the quant models defined herein    -   Extensibility of condition inspection capabilities    -   Extensibility of agents integrated with    -   Extensibility of compliance policies    -   Extensibility of policy enforcement actions    -   Ability to define compliance policies in terms of logical        combinations of conditions    -   Ability to define compliance policies in terms of quantitative        terms    -   Ability to define compliance policies for non quant conditions        in quant terms    -   Ability to combine analysis methods and create N-stage analysis        sequences    -   the notion of graduated levels of compliance and graduated        levels of local permissions/restrictions depending on your level        of compliance

While the invention has been shown and described with respect toparticular embodiments, it is not thus limited. Numerous modifications,changes, enhancements and improvements within the scope of the inventionwill now be apparent to the reader.

What is claimed is: 1-28. (canceled)
 29. A method for controlling theoperation of an endpoint, comprising: providing a user interface, at acomputing system that is remote from the endpoint, configured to allowconfiguration of a plurality of policies; maintaining the plurality ofpolicies in a data store on the computing system; identifying, from theplurality of policies, a plurality of operating conditions on theendpoint to evaluate; configuring one or more software services providedby an operating system on the endpoint to monitor the plurality ofoperating conditions; receiving, across a network, at the computingsystem, status information about the plurality of operating conditionson the endpoint, gathered by the one or more software services on theendpoint, and user information that identifies a user of the endpoint;determining, by the computing system, a compliance state of the endpointbased on the user information and status information, and a plurality ofcompliance policies in the data store; authorizing access by theendpoint to a computing resource on the network, authorization beingdetermined by the remote computing system in response to the compliancestate; and continuing to monitor the compliance state by the endpointand restricting access to the computing resource if the compliance statechanges.
 30. The method of claim 29, wherein the user interfacecomprises a web page.
 31. The method of claim 29, further comprisingrequesting, at the computing system, the status information on aperiodic basis.
 32. The method of claim 29, wherein the endpointcomprises a mobile device.
 33. The method of claim 29, furthercomprising configuring one or more applications running on the endpointon the endpoint to monitor at least a subset of the plurality ofoperating conditions.
 34. The method of claim 29, wherein the conditionscomprise at least one hardware condition.
 35. The method of claim 29,wherein the conditions comprise at least one software condition.
 36. Themethod of claim 29, wherein the computing system comprises a pluralityof servers.
 37. A non-transitory computer readable medium containingcomputer instructions for controlling the operation of an endpoint,comprising: providing a user interface, at a computing system that isremote from the endpoint, configured to allow configuration of aplurality of policies; maintaining the plurality of policies in a datastore on the computing system; identifying, from the plurality ofpolicies, a plurality of operating conditions on the endpoint toevaluate; configuring one or more software services provided by anoperating system on the endpoint to monitor the plurality of operatingconditions; receiving, across a network, at the computing system, statusinformation about the plurality of operating conditions on the endpoint,gathered by the one or more software services on the endpoint, and userinformation that identifies a user of the endpoint; determining, by thecomputing system, a compliance state of the endpoint based on the userinformation and status information, and a plurality of compliancepolicies in the data store; authorizing access by the endpoint to acomputing resource on the network, authorization being determined by theremote computing system in response to the compliance state; andcontinuing to monitor the compliance state by the endpoint andrestricting access to the computing resource if the compliance statechanges.
 38. The non-transitory computer readable medium of claim 37,wherein the user interface comprises a web page.
 39. The non-transitorycomputer readable medium of claim 37, further comprising requesting, atthe computing system, the status information on a periodic basis. 40.The non-transitory computer readable medium of claim 37, wherein theendpoint comprises a mobile device.
 41. The non-transitory computerreadable medium of claim 37, further comprising configuring one or moreapplications running on the endpoint on the endpoint to monitor at leasta subset of the plurality of operating conditions.
 42. Thenon-transitory computer readable medium of claim 37, wherein theconditions comprise at least one hardware condition.
 43. Thenon-transitory computer readable medium of claim 37, wherein theconditions comprise at least one software condition.
 44. Thenon-transitory computer readable medium of claim 37, wherein thecomputing system comprises a plurality of servers.
 45. A system forcontrolling the operation of an endpoint, comprising: a user interface,provided by a computing system remote from the end point, configured toallow configuration of a plurality of policies; a data store, at thecomputing system, that contains the plurality of policies; one or moresoftware services provided by an operating system on the endpointconfigured to evaluate a plurality of operating conditions identified inthe plurality of policies; and one or more hardware processors at thecomputing system configured to: receive, across a network, at thecomputing system, status information about the plurality of operatingconditions on the endpoint, gathered by the one or more softwareservices on the endpoint, and user information that identifies a user ofthe endpoint, determine, by the computing system, a compliance state ofthe endpoint based on the user information and status information, and aplurality of compliance policies in the data store, and authorize accessby the endpoint to a computing resource on the network, authorizationbeing determined by the remote computing system in response to thecompliance state.
 46. The system of claim 45, wherein the user interfacecomprises a web page.
 47. The system of claim 45, further comprisingrequesting, at the computing system, the status information on aperiodic basis.
 48. The system of claim 45, wherein the endpointcomprises a mobile device.
 49. The system of claim 45, furthercomprising configuring one or more applications running on the endpointon the endpoint to monitor at least a subset of the plurality ofoperating conditions.
 50. The system of claim 45, wherein the conditionscomprise at least one hardware condition.
 51. The system of claim 45,wherein the conditions comprise at least one software condition.
 52. Thesystem of claim 45, wherein the computing system comprises a pluralityof servers.